A curated list of awesome work on causal inference and (some) causal discovery, particularly in machine learning.
I am gathering resources (currently @ 271 papers) as literature for my PhD, and thought it may come in useful for others. This list includes work relating causal inference to deep learning, statistics, machine learning, and representation learning. If I've missed your paper, or there's a paper you want on the list, then feel free to contribute or email me : ]
The are ordered by year (new to old).
Identification of Latent Variables From Graphical Model Residuals. Hayete, Gruber, Decker, Yan. https://arxiv.org/pdf/2101.02332.pdf
Disentangling Observed Causal Effects from Latent Confounders using Method of Moments. Liu, Liu, Li, Karimi-Bidhendi, Yue, Anandkumar. https://arxiv.org/pdf/2101.06614.pdf
Counterfactual Generative Networks. Sauer, Geiger. https://arxiv.org/pdf/2101.06046.pdf
Model Compression for Domain Adaptation through Causal Effect Estimation. Rotman, Feder, Reichart. https://arxiv.org/pdf/2101.07086.pdf
Discrete Graph Structure Learning for Forecasting Multiple Time Series. Shang, Chen, Bi. https://arxiv.org/pdf/2101.06861.pdf
Instance-Specific Causal Bayesian Network Structure Learning. Jabbari. http://d-scholarship.pitt.edu/40018/19/Jabbari%20Final%20ETD.pdf
The limits of graphical causal discovery. Sevilla. https://towardsdatascience.com/the-limits-of-graphical-causal-discovery-92d92aed54d6?gi=db403d386344
Estimating Average Treatment Effects via Orthogonal Regularization. Hatt, Feuerriegel. https://arxiv.org/pdf/2101.08490.pdf
CDSM--Casual Inference using Deep Bayesian Dynamic Survival Models. Zhu, Gallego. https://arxiv.org/pdf/2101.10643.pdf
Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithms. Curth, van der Schaar. https://arxiv.org/pdf/2101.10943.pdf
Causality and independence in perfectly adapted dynamical systems. Blom, Mooij. https://arxiv.org/pdf/2101.11885.pdf
Causal inference for quantile treatment effects. Sun, Moodie, Neslehova. https://onlinelibrary.wiley.com/doi/abs/10.1002/env.2668
Variational Bayes survival analysis for unemployment modelling. Boskoski, Perne, Ramesa, Boshkoska. https://arxiv.org/pdf/2102.02295.pdf
A scoping review of causal methods enabling predictions under hypothetical interventions. Lin, Sperrin, Jenkins, Martin, Peek. https://link.springer.com/article/10.1186/s41512-021-00092-9
Estimating the treatment effect for adherers using multiple imputation. Luo, Ruberg, Qu. https://arxiv.org/pdf/2102.03499.pdf
On the Sample Complexity of Causal Discovery and the Value of Domain Expertise. Wadhwa, Dong. https://arxiv.org/pdf/2102.03274.pdf
Integer Programming for Causal Structure Learning in the Presence of Latent Variables. Chen, Dash, Gao. https://arxiv.org/pdf/2102.03129.pdf
Improving Causal Discovery By Optimal Bayesian Network Learning. Lu. Zhang, Yuan. https://www.aaai.org/AAAI21Papers/AAAI-8537.LuN.pdf
Estimating Identifiable Causal Effects through Double Machine Learning. Jung, Tian, Bareinboim. https://www.aaai.org/AAAI21Papers/AAAI-8987.JungY.pdf
Counterfactual Explanation with Multi-Agent Reinforcement Learning for Drug Target Prediction. Nguyen, Quinn, Nguyen, Tran. https://arxiv.org/pdf/2103.12983.pdf
User-oriented smart general AI system under causal inference. Peng. https://arxiv.org/pdf/2103.14561.pdf
A New Causal Approach to Account for Treatment Switching in Randomized Experiments under a Structural Cumulative Survival Model. Ying, Tchetgen. https://arxiv.org/pdf/2103.12206.pdf
Average Treatment Effects in the Presence of Interference. Hu, Li, Wager. https://arxiv.org/pdf/2104.03802.pdf
FRITL: A Hybrid Method for Causal Discovery in the Presence of Latent Confounders. Chen, Zhang, Cai, Huang, Ramsey, Hao, Glymour. https://arxiv.org/pdf/2103.14238.pdf
A brief introduction to causal inference. Nguyen. http://review.ttu.edu.vn/index.php/review/article/download/112/120&hl=en&sa=X&d=3868739188093254491&ei=S3B1YKqkKPqB6rQP7YGXiAI&scisig=AAGBfm2BnYjxbqbGRyi2ySV4VOsV383PQg&nossl=1&oi=scholaralrt&html=&folt=cit
Causal Decision Making and Causal Effect Estimation Are Not the Same... and Why It Matters. Fernandez-Loria, Provost. https://arxiv.org/pdf/2104.04103.pdf
Being bayesian about causal inference. Bucur. https://repository.ubn.ru.nl/bitstream/handle/2066/226922/226922.pdf?sequence=1&isAllowed=y
A computational model for complex systems analysis: Causality estimation. Sinha, Loparo. https://www.sciencedirect.com/science/article/abs/pii/S0167278921000737
Post-selection Problems for Causal Inference with Invalid Instruments: A Solution Using Searching and Sampling. Guo. https://arxiv.org/pdf/2104.06911.pdf
On the implied weights of linear regression for causal inference. Chattopadhyay, Zubizarreta. https://arxiv.org/pdf/2104.06581.pdf
Fast and effective pseudo transfer entropy for bivariate data-driven causal inference. Silini, Masoller. https://www.nature.com/articles/s41598-021-87818-3
Shadow-Mapping for Unsupervised Neural Causal Discovery. Vowels, Camgoz, Bowden. https://arxiv.org/pdf/2104.08183.pdf
Sequential Deconfounding for Causal Inference with Unobserved Confounders. Hatt, Feuerriegel. https://arxiv.org/pdf/2104.09323.pdf
CATE meets ML-The Conditional Average Treatment Effect and Machine Learning. Jacob. https://arxiv.org/pdf/2104.09935.pdf
A calculus for causal inference with instrumental variables. Wong. https://arxiv.org/pdf/2104.10633.pdf
Causal-TGAN: Generating Tabular Data Using Causal Generative Adversarial Networks. Wen, Colon, Subbalakshmi, Chandramouli. https://arxiv.org/pdf/2104.10680.pdf
Causal Discovery. Sucar https://link.springer.com/chapter/10.1007/978-3-030-61943-5_15
Understanding the causal structure among the tags in marketing systems. Zheng, Yang, Liu https://link.springer.com/article/10.1007/s00521-020-05552-9
Nonlinear Invariant Risk Minimization: A Causal Approach. Lu.Wu, Hernandez-Lobato, Scholkopf https://arxiv.org/pdf/2102.12353.pdf
Beware of the Simulated DAG! Varsortability in Additive Noise Models. Reisach, Seiler, Weichwald https://arxiv.org/pdf/2102.13647.pdf
Covariate balancing for causal inference on categorical and continuous treatments. Lee, Ma, de Luna https://arxiv.org/pdf/2103.00527.pdf
Toward Causal Representation Learning. Scholkopf, Locatello, Bauer, Ke, Kalchbrenner, Goyal, Bengio https://ieeexplore.ieee.org/document/9363924/?denied=
Improving Causal Inference by Increasing Model Expressiveness. Jensen https://www.aaai.org/AAAI21Papers/SMT-427.JensenD.pdf
A Generative Adversarial Framework for Bounding Confounded Causal Effects. Hu, Wu, Zhang, Wu https://www.aaai.org/AAAI21Papers/AAAI-3651.HuY.pdf
Why did the distribution change? Budhathoki, Janzing, Blobaum, Ng https://arxiv.org/pdf/2102.13384.pdf
Incorporating Causal Graphical Prior Knowledge into Predictive Modeling via Simple Data Augmentation. Teshima, Sugiyama https://arxiv.org/pdf/2103.00136.pdf
Regularizing towards Causal Invariance: Linear Models with Proxies. Oberst, Thams, Peters, Sontag https://arxiv.org/pdf/2103.02477.pdf
Relate and predict: Structure-Aware prediction with Jointly Optimized Neural DAG. Sekhon, Wang, Qi https://arxiv.org/pdf/2103.02405.pdf
Estimating Identifiable Causal Effects on Markov Equivalence Class through Double Machine Learning. Jung, Tian, Bareinboim https://causalai.net/r71.pdf
D?ya like DAGs? A Survey on Structure Learning and Causal Discovery. Vowels, Camgoz, Bowden https://arxiv.org/pdf/2103.02582.pdf
Non-Parametric Methods for Partial Identification of Causal Effects. Zhang, Bareinboim https://causalai.net/r72.pdf
Placebo Tests for Causal Inference. Eggers, Tunon, Dafoe https://pelg.ucsd.edu/Eggers_2021.pdf
Bayesian Doubly Robust Causal Inference via Loss Functions. Luo, Stephens, Graham, McCoy https://arxiv.org/pdf/2103.04086.pdf
Causality indices for bivariate time series data: a comparative review of performance. Edinburgh, Eglen, Ercole https://arxiv.org/pdf/2104.00718.pdf
Doubly robust confidence sequences for sequential causal inference. Waudby-Smith, Arbour, Sinha, Kennedy, Ramdas https://arxiv.org/pdf/2103.06476.pdf
Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding. Jesson, Mindermann, Gal, Shalit https://arxiv.org/pdf/2103.04850.pdf
A Tree-based Federated Learning Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources. Tan, Chang, Tang https://arxiv.org/pdf/2103.06261.pdf
Time-Reversibility, Causality and Compression-Complexity. Kathpalia, Nagaraj.
Identifiability of causal effects with multiple causes and a binary outcome. Kong, Yang, Wang https://academic.oup.com/biomet/advance-article-abstract/doi/10.1093/biomet/asab016/6168988
Three Essays on Model Selection in Time Series Econometrics. Aka https://refubium.fu-berlin.de/handle/fub188/29740
Causal Inference Q-Network: Toward Resilient Reinforcement Learning. Yang, Hung, Ouyang, Chen https://arxiv.org/pdf/2102.09677.pdf
Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties. Schut, Key, McGrath, Costabello, Sacaleanu, Corcoran, Gal https://arxiv.org/pdf/2103.08951.pdf
Treatment Effect Estimation using Invariant Risk Minimization. Shah, Ahuja, Shanmugam, Wei, Varshney, Dhurandhar https://arxiv.org/pdf/2103.07788.pdf
VCNet and Functional Targeted Regularization For Learning Causal Effects of Continuous Treatments. Nie, Ye, Liu, Nicolae https://arxiv.org/pdf/2103.07861.pdf
Uplift Modeling: from Causal Inference to Personalization. Teinemaa, Albert, Goldenberg https://www.researchgate.net/profile/Dmitri-Goldenberg/publication/349762341_Uplift_Modeling_from_Causal_Inference_to_Personalization/links/60409ccca6fdcc9c780f8b37/Uplift-Modeling-from-Causal-Inference-to-Personalization.pdf
Higher-Order Orthogonal Causal Learning for Treatment Effect. Huang, Leung, Yan, Wu https://arxiv.org/pdf/2103.11869.pdf
NCoRE: Neural Counterfactual Representation Learning for Combinations of Treatments. Parbhoo, Bauer, Schwab https://arxiv.org/pdf/2103.11175.pdf
Causal Inference under Networked Interference and Intervention Policy Enhancement. Ma, Tresp http://proceedings.mlr.press/v130/ma21c/ma21c.pdf
Causal Modeling with Stochastic Confounders. Vo, Wei, Bergsma, Leong http://proceedings.mlr.press/v130/vinh-vo21a/vinh-vo21a.pdf
ANOCE: analysis of causal effects with multiple mediators via constrained structural learning. Cai, Song, Lu https://openreview.net/pdf/8142413a92e7df5fb79598dee863640346d53f5b.pdf
Causal Inference Under Unmeasured Confounding With Negative Controls: A Minimax Learning Approach. Kallus, Mao, Uehara https://arxiv.org/pdf/2103.14029.pdf
Conditions and Assumptions for Constraint-based Causal Structure Learning. Sadeghi, Soo https://arxiv.org/pdf/2103.13521.pdf
Robust estimation of heterogeneous treatment effects using electronic health record data. Li, Wang, Tu https://onlinelibrary.wiley.com/doi/full/10.1002/sim.8926
Causal Discovery with Bijective Fixed-Cause Functionals. Jalaldoust, Salehkaleybar, Kiyavash. http://jalaldoust.com/docs/CDBFF.pdf
User-Oriented Smart General AI System under Causal Inference. Peng https://arxiv.org/pdf/2103.14561.pdf
Deconfounded Score Method: Scoring DAGs with Dense Unobserved Confounding. Bellot, van der Schaar https://arxiv.org/pdf/2103.15106.pdf
A New Causal Approach to Account for Treatment Switching in Randomized Experiments under a Structural Cumulative Survival Model. Ying, Tchetgen https://arxiv.org/pdf/2103.12206.pdf
SEMgraph: An R Package for Causal Network Analysis of High-Throughput Data with Structural Equation Models. Paluzzi, Grassi https://arxiv.org/pdf/2103.08332.pdf
Bayesian optimal experimental design for inferring causal structure. Zemplenyi, Miller https://arxiv.org/pdf/2103.15229.pdf
Multi-Source Causal Inference Using Control Variates. Guo, Wang, Ding, Wang, Jordan https://arxiv.org/pdf/2103.16689.pdf
Intact-VAE: Estimating Treatment Effects under Unobserved Confounding. Wu, Fukumizu https://arxiv.org/pdf/2101.06662.pdf
The Impact of Time Series Length and Discretization on Longitudinal Causal Estimation Methods. Adams, Saria, Rosenblum. https://arxiv.org/pdf/2011.15099.pdf
algcomparison: Comparing the Performance of Graphical Structure Learning Algorithms with TETRAD. Ramsey, Malinsky, Bui https://www.jmlr.org/papers/volume21/19-773/19-773.pdf
Towards causality-aware predictions in static anticausal machine learning tasks: the linear structural causal model case. Neto https://www.cmu.edu/dietrich/causality/CameraReadys-accepted%20papers/23%5CCameraReady%5Ccdml_causality_aware.pdf
Evaluation of Algorithm Selection and Ensemble Methods for Causal Discovery. Saldanha, Cosbey, Ayton, Glenski, Cottam, Shivaram, Jefferson, Hutchinson, Arendt, Volkova https://www.cmu.edu/dietrich/causality/CameraReadys-accepted%20papers/28%5CCameraReady%5CEvaluating_Causal_Ensembles_NeurIPS_CR.pdf
Causal World Models by Unsupervised Deconfounding of Physical Dynamics. Li, Yang, Liu, Chen, Chen, Wang https://arxiv.org/pdf/2012.14228.pdf
Intervention Efficient Algorithms for Approximate Learning of Causal Graphs. Addanki, McGregor, Musco https://arxiv.org/pdf/2012.13976.pdf
Amortized learning of neural causal representations. Ke, Wang, Mitrovic, Szummer, Rezende https://arxiv.org/pdf/2008.09301.pdf
Causal future prediction in a Minkowski space-time. Vlontzos, Rocha, Rueckert, Kainz https://arxiv.org/pdf/2008.09154.pdf
Path dependent structural equation models. Srinivasan, Lee, Ahmidi, Shpitser https://arxiv.org/pdf/2008.10706.pdf
A narrative review of methods for causal inference and associated educational resources. Landsittel, Srivastava, Kropf, Kristin https://journals.lww.com/qmhcjournal/Abstract/2020/10000/A_Narrative_Review_of_Methods_for_Causal_Inference.12.aspx?context=LatestArticles
Targeted VAE: structured inference and targeted learning for causal parameter estimation. Vowels, Camgoz, Bowden https://arxiv.org/pdf/2009.13472.pdf
CASTLE: regularization via auxiliary causal graph discovery. Kyono, Zhang https://arxiv.org/pdf/2009.13180.pdf
Identifying treatment effects under unobserved confounding by causal representation learning. Anonymous https://openreview.net/forum?id=D3TNqCspFpM
Causal discovery for causal bandits utilizing separating sets. de Kroon, Belgrave, Mooij https://arxiv.org/pdf/2009.07916.pdf
Learning DAGs with continuous optimization. Zheng https://www.ml.cmu.edu/research/phd-dissertation-pdfs/thesis-zheng-xun.pdf
Hybridizing machine learning methods and finite mixture models for estimating heterogeneous treatment effects in latent classes. Suk, Kim, Kang https://journals.sagepub.com/doi/abs/10.3102/1076998620951983
Tuning causal discovery algorithms. Biza, Tsamardinos, Triantafillou https://pgm2020.cs.aau.dk/wp-content/uploads/2020/09/biza20.pdf
Debiased machine learning of conditional average treatment effects and other causal functions. Semenova, Chernozhukov https://academic.oup.com/ectj/advance-article-abstract/doi/10.1093/ectj/utaa027/5899048
Estimating individual treatment effects with time-varying confounders. Liu, Yin, Zhang https://arxiv.org/pdf/2008.13620.pdf
Confounding feature acquisition for causal effect estimation. Wang, Yi, Joshi, Ghassemi https://arxiv.org/pdf/2011.08753.pdf
Causal inference methods for combining randomized trials and observational studies: a review. Colnet, Mayer, Chen, Dieng, Li, Varoquaux, Vert, Josse, Yang https://arxiv.org/pdf/2011.08047.pdf
Reconstruction of a directed acyclic graph with intervention. Peng, Shen https://projecteuclid.org/download/pdfview_1/euclid.ejs/1605582080
Debiased Inverse Propensity Score Weighting for Estimation of Average Treatment Effects with High-Dimensional Confounders. Wang, Shah https://arxiv.org/pdf/2011.08661.pdf
A novel method for Causal Structure Discovery from EHR data. Shen, Ma, Vemuri, Castro, Caraballo, Simon https://arxiv.org/pdf/2011.05489.pdf
Teaching deep learning causal effects improves predictive performance. Li, Jia, Yang, Kumar, Steinbach, Simon https://arxiv.org/pdf/2011.05466.pdf
Learning Causal Representations for Robust Domain Adaptation. Yang, Yu, Cao, Liu, Wang, Li https://arxiv.org/pdf/2011.06317.pdf
Learning causal semantic representations for out-of-distribution prediction. Liu, Sun, Wang, Li, Qin, Chen, Liu https://arxiv.org/pdf/2011.01681.pdf
Counterfactual Fairness with disentangled causal effect variational autoencoder. Kim, Shin, Jang, Song, Joo, Kang, Moon https://arxiv.org/pdf/2011.11878.pdf
A systematic review of causal methods enabling predictions under hypothetical interventions. Lin, Sperrin, Jenkins, Martin, Peek https://arxiv.org/pdf/2011.09815.pdf
Efficient permutation discovery in causal DAGs. Squires, Amaniampong, Uhler https://arxiv.org/pdf/2011.03610.pdf
Causality-aware counterfactual confounding adjustment as an alternative to linear residualization in anticausal prediction tasks based on linear learners. Neto https://arxiv.org/pdf/2011.04605.pdf
Conditional independence testing for variable selection and causal inference. Bates https://search.proquest.com/openview/f46a5071aecc21df0cbb3f43d408bcfd/1?pq-origsite=gscholar&cbl=18750&diss=y
Interpretable models for Granger causality using self-explaining neural networks. Marcinkevics, Vogt https://mds.inf.ethz.ch/fileadmin/user_upload/gc_neurips2020_workshop_cr.pdf
High-dimensional feature selection for sample efficient treatment effect estimation. Greenewald, Katz-Rogozhnikov, Shanmugam https://arxiv.org/pdf/2011.01979.pdf
Latent causal invariant model. Sun, Wu, Liu, Zheng, Chen, Qin, Liu https://arxiv.org/pdf/2011.02203.pdf
Applications of common entropy for causal inference. Kocaoglu, Shakkottai, Dimakis, Caramanis, Vishwanath https://proceedings.neurips.cc/paper/2020/file/cae7115f44837c806c9b23ed00a1a28a-Paper.pdf
Entropic causal inference: identifiability and finite sample results. Compton, Kocaoglu, Greenewald, Katz https://proceedings.neurips.cc/paper/2020/file/a979ca2444b34449a2c80b012749e9cd-Paper.pdf
Generalized independent noise condition for estimating latent variable causal graphs. Xie, Cai, Huang, Glymour, Hao, Zhang https://proceedings.neurips.cc/paper/2020/file/aa475604668730af60a0a87cc92604da-Paper.pdf
Bayesian causal structural learning with zero-inflated poisson bayesian networks. Choi, Chapkin, Ni https://proceedings.neurips.cc/paper/2020/file/4175a4b46a45813fccf4bd34c779d817-Paper.pdf
Causal autoregressive flows. Khemakhem, Monti, Leech, Hyvarinen https://arxiv.org/pdf/2011.02268.pdf
Causal variables from reinforcement learning using generalized Bellman equations. Herlau https://arxiv.org/pdf/2010.15745.pdf
Domain adaptation under structural causal models. Chen, Buhlmann https://arxiv.org/pdf/2010.15764.pdf
Causalworld: A robotic manipulation benchmark for causal structure and transfer learning. Ahmed, Trauble, Goyal, Neitz, Bengio, Scholkopf, Bauer, Wuthrich https://arxiv.org/pdf/2010.04296.pdf
Representation learning for treatment effect estimation. Yao https://search.proquest.com/openview/d21f343b17412c1af8099ac93ae92fee/1?pq-origsite=gscholar&cbl=18750&diss=y
Neural additive vector autoregression models for causal discovery in time series data. Bussmann, Nys, Latre https://arxiv.org/pdf/2010.09429.pdf
Causal discovery using compression-complexity measures. SY, Nagaraj https://arxiv.org/pdf/2010.09336.pdf
DAGs with no fears: a closer look at continuous optimization for learning bayesian networks. Wei, Gao, Yu https://arxiv.org/pdf/2010.09133.pdf
Learning robust models using the principle of independent causal mechanisms. Muller, Schmier, Ardizzone, Rother, Kothe https://arxiv.org/pdf/2010.07167.pdf
Double robust representation learning for counterfactual prediction. Zeng, Asaad, Tao, Datta, Carin, Li https://arxiv.org/pdf/2010.07866.pdf
Causal learning with sufficient statistics: an information bottleneck approach. Chicharro, Besserve, Panzeri https://arxiv.org/pdf/2010.05375.pdf
Differentiable causal discovery under unmeasured confounding. Bhattacharya, Nagarajan, Malinsky, Shpitser https://arxiv.org/pdf/2010.06978.pdf
Identifying causal-effect inference failure with uncertainty-aware models. Jesson, Mindermann, Shalit, Gal https://arxiv.org/abs/2007.00163
Causally correct partial models for reinforcement learning. Rezende, Danihelka, Papamakarios, Ke, Jiang, Webever, Gregor, Merzic, Viola, Wang, Mitrovic, Besse, Antonoglou, Buesing https://arxiv.org/pdf/2002.02836v1.pdf
Causal curiosity: RL agents discovering self-supervised experiments for causal representation learning. Sontakke, Mehrjou https://arxiv.org/pdf/2010.03110.pdf
Assessing the fairness of classifiers with collider bias. Xi, Liu, Cheng, Li, Liu, Kang https://arxiv.org/pdf/2010.03933.pdf
Disentangling causal effects for hierarchical reinforcement learning. Corcoll. Vicente https://arxiv.org/pdf/2010.01351.pdf
A new representation learning method for individual treatment effect estimation: split covariate representation network. Liu, Tian, Ji, Zheng http://proceedings.mlr.press/v129/qidong20a/qidong20a.pdf
Personalized estimation and causal inference via deep learning algorithms. Liu https://digitalcommons.library.tmc.edu/cgi/viewcontent.cgi?article=1149&context=uthsph_dissertsopen
Disentangled generative causal representation learning. Shen, Liu, Dong, Lian, Chen, Zhang https://arxiv.org/pdf/2010.02637.pdf
Explaining the efficacy of counterfactually-augmented data. Kaushik, Setlur, Hovy, Lipton https://arxiv.org/pdf/2010.02114.pdf
A new framework for causal discovery. van Leeuwen, DeCaria, Chakaborty, Pulido https://arxiv.org/pdf/2010.02247.pdf
Graphical Granger causality by information-theoretic criteria. Hlavackova-Schindler, Plant http://eprints.cs.univie.ac.at/6518/1/264_paper.pdf
Systematic evaluation of causal discovery in visual model based reinforcement learning anonymous https://openreview.net/pdf/fd60f3b99ed8b26cd60f5f884fe2e6eb7e3ec327.pdf
Long-term effect estimation with surrogate representation. Cheng, Guo, Liu https://arxiv.org/pdf/2008.08236.pdf
Heidegger: Interpretable temporal causal discovery. Mansouri, Arab, Zohrevand, Ester https://dl.acm.org/doi/abs/10.1145/3394486.3403220
Deconfounding and causal regularization for stability and external validity. Buhlmann, Cevid https://arxiv.org/pdf/2008.06234.pdf
A Bayesian nonparametric conditional two-sample test with an application to local causal discovery. Boeken, Mooij https://arxiv.org/pdf/2008.07382.pdf
Semiparametric estimation and inference on structural target functions using machine learning and influence functions. Curth, Alaa, Schaar, https://arxiv.org/pdf/2008.06461.pdf
Estimating causal effects with the neural autoregressive density estimator. Garrido, Borysov, Rich, Pereira https://arxiv.org/pdf/2008.07283.pdf
Reparametrization invariance for non-parametric causal discovery. Jorgensen, Hauberg https://arxiv.org/pdf/2008.05552.pdf
Multivariate counterfactual systems and causal graphical models. Shpitser, Richardson, Robins https://arxiv.org/pdf/2008.06017.pdf
Causal inference on discrete data. Budhathoki https://eda.mmci.uni-saarland.de/pubs/2020/phd-budhathoki.pdf
CRUDS: Counterfactual recourse using disentangled subspaces. Downs, Chu, Yacoby, Doshi-Velez, Pan https://finale.seas.harvard.edu/files/finale/files/cruds-_counterfactual_recourse_using_disentangled_subspaces.pdf
A causal lens for peeking into black box predictive models: predictive model interpretation via causal attribution. Khademi, Honava https://arxiv.org/pdf/2008.00357.pdf
Improving and assessing causal inference algorithms for DAGs. Eigenmann https://www.research-collection.ethz.ch/bitstream/handle/20.500.11850/428225/1/DoctoralthesisMarcoEigenmann.pdf
Differentiable causal discovery from interventional data. Brouillard, Lachapelle, Lacoste, Lacoste-Julien, Drouin https://arxiv.org/pdf/2007.01754.pdf
Causal feature selection via orthogonal search. Raj, Bauer, Soleymani, Besserve https://arxiv.org/pdf/2007.02938.pdf
High-recall causal discovery for autocorrelated time series with latent confounders. Gerhardus, Runge https://arxiv.org/pdf/2007.01884.pdf
Adapting text embeddings for causal inference. Veitch, Sridhar, Blei http://www.auai.org/uai2020/proceedings/376_main_paper.pdf
Flagpoles anyone? Causal and explanatory asymmetries. Woodward http://philsci-archive.pitt.edu/17419/1/flagpoles2%20with%20changes%20accepted%207.1.20.pdf
IGNITE: a minimax game toward learning individual treatment effects from networked observational data. Guo, Li, Li, Candan, Raglin, Liu http://www.public.asu.edu/~rguo12/IJCAI20_IGNITE_arxiv.pdf
Counterfactual propagation for semi-supervised individual treatment effect estimation. Harada, Kashima https://arxiv.org/pdf/2005.05099.pdf
Simpson's paradox in COVID-19 case fatality rates: a mediation analysis of age-related causal effects. von Kugelgen, Gresele, Scholkopf https://arxiv.org/abs/2005.07180
Counterfactual confounding adjustment for feature representationas learned by deep models: with an application to image classification tasks. Neto https://arxiv.org/abs/2004.09466
Necessary and sufficient conditions for causal feature selection in time series with latent common causes. Mastakouri, Scholkopf, Janzing https://arxiv.org/pdf/2005.08543.pdf
Achieving causal fairness in machine learning. Wu https://search.proquest.com/openview/5b94283bd4da8edc1b14bff3db4c9e77/1?pq-origsite=gscholar&cbl=18750&diss=y
Efficient intervention design for causal discovery with latents. Addanki, Kasiviswanathan, McGregor, Musco https://arxiv.org/pdf/2005.11736.pdf
CausaLM: Causal model explanation through counterfactual language models. Feder, Oved, Shalit, Reichart https://arxiv.org/pdf/2005.13407.pdf
Bayesian network structure learning with causal effects in the presence of latent variables. Chobtham, Constantinou https://arxiv.org/pdf/2005.14381.pdf
A principled approach to multiple causal inference. Mehta https://static1.squarespace.com/static/577d2a80579fb35f94742dbb/t/5eb43f0b8a1cef37852cc5c3/1588870930090/senior_thesis.pdf
Study causal inference techniques for data-driven personalised decision-making. Dai https://vrs.amsi.org.au/wp-content/uploads/sites/75/2020/01/dai_zhou_vrs-report.pdf
Phenomenal causality and sensory realism. Mding, Bruins, Scholkopf, Berens,Wichmann https://journals.sagepub.com/doi/pdf/10.1177/2041669520927038
Causal inference with deep causal graphs. Parafita, Vitria https://arxiv.org/pdf/2006.08380.pdf
Is independence all your need? On the generalization of representations learned from correlated data. Trauble, Creager, Kilbertus, Goyal, Locatello, Scholkopf, Bauer https://arxiv.org/pdf/2006.07886.pdf
Learning decomposed representation for counterfactual inference. Wu, Kuang, Yuan, Li, Zhou https://arxiv.org/pdf/2006.07040.pdf
Robust recursive partitioning for heterogeneous treatment effects with uncertainty quantification. Lee, Zhang, Zame, et al. https://arxiv.org/pdf/2006.07917.pdf
Learning causal models online. Javed, White, Bengio https://arxiv.org/pdf/2006.07461.pdf
Targeted learning: robust statistics for reproducible research. Coyle, Hejazi et al. https://arxiv.org/pdf/2006.07333.pdf
Supervised whole DAG causal discovery. Li, Xiao, Tian https://arxiv.org/pdf/2006.04697.pdf
Causal discovery from incomplete data using an encoder and reinforcement learning . Huang, Zhu, Holloway, Haidar https://arxiv.org/pdf/2006.05554.pdf
Identifying causal structure in dynamical systems. Baumann, Solowjow, Johansson, Trimpe https://arxiv.org/pdf/2006.03906.pdf
Optimal configuration of concentrating solar power in multienergy power systems with an improved variational autoencoder. Qi, Hu, Dong, Fan, Dong, Xiao https://www.sciencedirect.com/science/article/abs/pii/S030626192030636X
tvGP-VAE: tensor-variate gaussian process prior variational autoencoder. Campbell, Lio https://arxiv.org/pdf/2006.04788.pdf
OC-FakeDect: classifying deepfakes using one-class variational autoencoder. Khalid, Woo http://openaccess.thecvf.com/content_CVPRW_2020/papers/w39/Khalid_OC-FakeDect_Classifying_Deepfakes_Using_One-Class_Variational_Autoencoder_CVPRW_2020_paper.pdf
Tuning a variational autoencoder for data accountability problem in the Mars science laboratory ground data system. Lakhmiri, Alimo, Le Digabel https://arxiv.org/pdf/2006.03962.pdf
Counterfactual vision and language learning. Abbasnejad, Teneh, Parvaneh, Shi, Hengel http://openaccess.thecvf.com/content_CVPR_2020/papers/Abbasnejad_Counterfactual_Vision_and_Language_Learning_CVPR_2020_paper.pdf
Invariant risk minimization. Arjovsky, Bottou, Gulrajani, Lopez-Paz https://arxiv.org/pdf/1907.02893.pdf
Amortized causal discovery: learning to infer causal graphs from time-series data. Lowe, Madras, Zemel, Welling https://arxiv.org/abs/2006.10833
Structural autoencoders improve representations for generation and transfer. Leeb, Annadani, Bauer, Scholkopf https://arxiv.org/pdf/2006.07796.pdf
Recurrent independent mechanisms. Goyal, Lamb, Hoffmann, Sodhani, Levine, Bengio, Scholkopf https://arxiv.org/abs/1909.10893
A crash course in good and bad controls. Cinelli, Forney, Pearl https://ftp.cs.ucla.edu/pub/stat_ser/r493.pdf
A ladder of causal distances. Peyrard, West https://arxiv.org/pdf/2005.02480.pdf
Off-the-shelf deep learning is not enough: parsimony, Bayes and causality . Vasudevan, Ziatdinov, Vlcek, Kalinin https://arxiv.org/pdf/2005.01557.pdf
Gradient-based neural DAG learning with interventions. Brouillard, Drouin, Lachapelle, Lacoste, Lacoste-Julien https://causalrlworkshop.github.io/pdf/CLDM_10.pdf
Learning transferable task schemas by representing causal invariances. Madarasz, Behrens https://causalrlworkshop.github.io/pdf/CLDM_25.pdf
Designing data augmentation for simulating interventions. Ilse, Tomczak, Forre https://arxiv.org/pdf/2005.01856.pdf
A causal view n robustness of neural networks. Zhang, Zhang, Li https://arxiv.org/pdf/2005.01095.pdf
Estimation of post-nonlinear causal models using autoencoding structure. Uemura, Shimizu https://ieeexplore.ieee.org/abstract/document/9053468/
Potential outcome and directed acyclic graph approaches to causality: relevance for empirical practice in economics. Imbens https://arxiv.org/abs/1907.07271
Causal inference analysis with neural networks. Alonso https://j1nma.com/documents/JuanManuelAlonso-MTPaper.pdf
A survey on causal inference. Yao, Chu, Li, Li, Gao, Zhang https://arxiv.org/abs/2002.02770
MissDeepCausal: causal inference from incomplete data using deep latent variable models. Mayer, Josse, Raimundo, Vert https://arxiv.org/abs/2002.10837
Accurate data-driven prediction does not mean high reproducibility. Li, Liu, Le, Liu https://www.nature.com/articles/s42256-019-0140-2?proof=t
Causal models for dynamical systems. Peters, Bauer, Pfister https://arxiv.org/pdf/2001.06208.pdf
A critical view of the structural causal model. Galanti, Nabati, Wolf https://arxiv.org/abs/2002.10007
Treatment effect estimation with disentangled latent factors anon https://arxiv.org/abs/2001.10652
CausalVAE: structured causal disentanglement in variational autoencoder. Yang, Liu, Chen, Shen, Hao, Wang https://arxiv.org/pdf/2004.08697.pdf
A robust method for estimating individualized treatment effect. Meng, Qiao https://arxiv.org/pdf/2004.10108.pdf
Dark, beyond deep: a paradigm shift to cognitive AI with humanlike common sense. Zhu, Gao, Fan, Huang, Edmonds, Liu, Gao, Zhang, Qi, Wu, Tenenbaum, Zhu https://www.sciencedirect.com/science/article/pii/S2095809920300345
MultiMBNN: matched and balanced causal inference with neural networks Sharma, Gupta, Prasad, Chatterjee, Vig, Shroff https://arxiv.org/abs/2004.13446
Fairness by learning orthogonal disentangled representations. Sarhan, Navab, Eslami, Albarquouni https://arxiv.org/abs/2003.05707
Bounding causal effects on continuous outcomes. Zhang, Bareinboim https://causalai.net/r61.pdf
Interpretable subgroup discovery in treatment effect estimation with application to opioid prescribing guidelines. Nagpal, Wei, Vinzamuri et al https://arxiv.org/abs/1905.03297
DebFace: De-biasing face recognition, Gong, Liu, Jain https://arxiv.org/abs/1911.08080
Learning individual causal effects from networked observational data. Guo, Li, Liu https://arxiv.org/abs/1906.03485
Machine learning in policy evaluation new tools for causal inference Kreif, DiazOrdaz https://arxiv.org/abs/1903.00402
Counterfactual regression with importance sampling weights. Hassanpour, Greiner https://www.ijcai.org/Proceedings/2019/0815.pdf
The causal structure of suppressor variables. Kim https://journals.sagepub.com/doi/10.3102/1076998619825679
A survey of learning causality with data: problems and methods Guo, Cheng, Li, Hahn, Liu https://arxiv.org/abs/1809.09337
Perfect match: a simple method for learning representations for counterfactual inference with neural networks. Schwab, Linhardt, Karlen https://arxiv.org/abs/1810.00656
Causality matters in medical imaging. Castro, Walker, Glocker https://arxiv.org/abs/1912.08142
Reducing selection bias in counterfactual reasoning for individual treatment effects estimation. Zhang, Lan, Ding, Wang, Hassanpour, Greiner https://arxiv.org/abs/1912.09040
Two causal principles for improving visual dialog. Qi, Niu, Huang, Zhang https://arxiv.org/pdf/1911.10496.pdf
Learning disentangled representations for counterfactual regression. Hassanpour, Greiner https://openreview.net/pdf?id=HkxBJT4YvB
Understanding human judgments of causality. Kazama, Suhara, Bogomolov, Pentland https://arxiv.org/pdf/1912.08998.pdf
Graphical causal models for survey inference Schuessler, Selb https://osf.io/preprints/socarxiv/hbg3m/
Learning counterfactual representations for estimating individual dose-response curves. Schwab, Linhardt, Bauer, Buhmann, Karlen https://arxiv.org/abs/1902.00981
Causal discovery toolbox: uncover causal relationships in Python. Kalainathan, Goudet https://arxiv.org/abs/1903.02278
Causal inference and data-fusion in econometrics. Hunermund, Bareinboim https://arxiv.org/pdf/1912.09104.pdf
Adapting neural networks for the estimation of treatment effects Shi, Blei, Veitch https://arxiv.org/pdf/1906.02120.pdf
Copy, paste, infer: a robust analysis of twin networks for counterfactual inference. Graham, Lee, Perov https://cpb-us-w2.wpmucdn.com/sites.coecis.cornell.edu/dist/a/238/files/2019/12/Id_65_final.pdf
Challenging the hegemony of randomized controlled trails. Pearl https://www.ncbi.nlm.nih.gov/pubmed/29704961
Understanding and misunderstanding randomized controlled trials. Deaton, Cartwright https://www.sciencedirect.com/science/article/pii/S0277953617307359
Using latent variable models to improve causal estimation. Oktay https://scholarworks.umass.edu/cgi/viewcontent.cgi?article=2241&context=dissertations_2
Learning representations for counterfactual inference. Johansson, Shalit, Sontag https://arxiv.org/abs/1605.03661
Representation learning for treatment effect estimation from observational data. Yao, Li, Li, Huai, Gao, Zhang https://papers.nips.cc/paper/7529-representation-learning-for-treatment-effect-estimation-from-observational-data.pdf
GANITE: Estimation of individualized treatment effects using generative adversarial nets. Yoon, Jordan, van der Schaar https://openreview.net/forum?id=ByKWUeWA-
Causal reasoning for algorithmic fairness. Loftus, Russell, Kusner, Silva https://arxiv.org/pdf/1805.05859.pdf
Granger-causal attentive mixtures of experts: learning important features with neural networks. Schwab, Miladinovic, Karlen https://arxiv.org/pdf/1802.02195.pdf
Structural causal bandits: where to intervene? Lee, Bareinboim https://causalai.net/r36.pdf
Counterfactual fairness. Kusner, Loftus, Russell, Silva https://papers.nips.cc/paper/6995-counterfactual-fairness
Avoiding discrimination through causal reasoning. Kilbertus, Rojas-Carulla, Parascandolo, Hardt, Janzing, Scholkopf https://arxiv.org/abs/1706.02744
Elements of causal inference. Peters, Janzing, Scholkopf https://mitpress.mit.edu/books/elements-causal-inference
Deep counterfactual networks with propensity-dropout. Alaa, Weisz, van der Schaar https://arxiv.org/abs/1706.05966
Estimating individual treatment effect: generalization bounds and algorithms. Shalit, Johansson, Sontag https://arxiv.org/pdf/1606.03976.pdf
Causal effect inference with deep latent-variable models. Louizos, Shalit, Mooij, Sontag, Zemel, Welling https://arxiv.org/pdf/1705.08821.pdf
When worlds collide: integrating different counterfactual assumptions in fairness. Russell, Kusner, Loftus, Silva https://papers.nips.cc/paper/7220-when-worlds-collide-integrating-different-counterfactual-assumptions-in-fairness.pdf
Recursive partitioning for heterogeneous causal effects. Athey, Imbens https://www.pnas.org/content/pnas/113/27/7353.full.pdf
Double/debiased machine learning for treatment and structural parameters. Chernozhukov, Chetverikov, Demirer, Duflo, Hansen, Newey, Robins https://economics.mit.edu/files/12538
Targeted maximum likelihood estimation for causal inference in observational studies. Schuler, Rose https://academic.oup.com/aje/article/185/1/65/2662306
Causal inference in law: an epidemiological perspective. Siegerink, Hollander, Zeegers, Middelburg https://www.cambridge.org/core/journals/european-journal-of-risk-regulation/article/causal-inference-in-law-an-epidemiological-perspective/316A2F893F03FCA836354BCC21BDC1E4
Equality of opportunity in supervised learning. Hardt, Price, Srebro https://papers.nips.cc/paper/6374-equality-of-opportunity-in-supervised-learning.pdf
Do-calculus when the true graph is unknown. Hyttinen, Eberhardt, Jarvisalo
Exploring causal relationships in visual object tracking. Lebeda, Hadfield, Bowden http://openaccess.thecvf.com/content_iccv_2015/papers/Lebeda_Exploring_Causal_Relationships_ICCV_2015_paper.pdf
Bandits with unobserved confounders: a causal approach. Bareinboim, Forney, Pearl https://ftp.cs.ucla.edu/pub/stat_ser/r460.pdf
Entering the era of data science: targeted learning and the integration of statistics and computational data analysis. van der Laan, Starmans https://www.hindawi.com/journals/as/2014/502678/
Seeing the arrow of time. Pickup, Pan, Wei, Shih, Zhang, Zisserman, Scholkopf, Freeman https://www.robots.ox.ac.uk/~vgg/publications/2014/Pickup14/pickup14.pdf
Causal diagrams for interference. Ogburn, VanderWeele https://arxiv.org/pdf/1403.1239.pdf
Causal models and learning from data: integrating causal modeling and statistical estimation. Petersen, van der Laan https://journals.lww.com/epidem/Fulltext/2014/05000/Causal_Models_and_Learning_from_Data__Integrating.13.aspx
Counterfactual reasoning and learning systems: the example of computational advertising. Bottou, Peters, Quinonero-Candela, Charles, Chickering, Portugaly, Ray, Simard, Snelson https://www.microsoft.com/en-us/research/wp-content/uploads/2013/11/bottou13a.pdf
A sound and complete algorithm for learning causal models from relational data. Maier, Marazopoulou, Arbour, David https://arxiv.org/abs/1309.6843
On a class of bias-amplifying variables that endanger effect estimates. Pearl https://arxiv.org/abs/1203.3503
Quantifying causal influences Janzing, Balduzzi, Grosse-Wentrup, Scholkopf https://arxiv.org/pdf/1203.6502.pdf http://webdav.tuebingen.mpg.de/causality/
Information flows in causal networks. Ay, Polani https://sfi-edu.s3.amazonaws.com/sfi-edu/production/uploads/sfi-com/dev/uploads/filer/45/5f/455fd460-b6b0-4008-9de1-825a5e2b9523/06-05-014.pdf
Separated at birth: statisticians, social scientists, and causality in health services research. Dowd https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3064910/
Proabilistic latent variable models for distinguishing between cause and effect. Mooij, Stegle, Janzing, Zhang, Scholkopf https://papers.nips.cc/paper/4173-probabilistic-latent-variable-models-for-distinguishing-between-cause-and-effect.pdf
In the light of time. Tuisku, Pernu, Annila https://royalsocietypublishing.org/doi/pdf/10.1098/rspa.2008.0494
Causal inference using potential outcomes: design, modeling, decisions. Rubin https://www.jstor.org/stable/2335942
Does matching overcome LaLonde's critique of nonexperimental estimators? Smith, Todd https://www.sciencedirect.com/science/article/abs/pii/S030440760400082X
The central role of the propensity score in observational studies for causal effects. Rosenbaum, Rubin https://www.jstor.org/stable/2335942