diff --git a/docs/source/example_notebooks/dowhy_simple_example.ipynb b/docs/source/example_notebooks/dowhy_simple_example.ipynb index d9aa8ec312..dc855d708b 100755 --- a/docs/source/example_notebooks/dowhy_simple_example.ipynb +++ b/docs/source/example_notebooks/dowhy_simple_example.ipynb @@ -50,19 +50,19 @@ "name": "stdout", "output_type": "stream", "text": [ - " X0 Z0 Z1 W0 W1 W2 W3 W4 v0 \\\n", - "0 -1.623410 0.0 0.336692 -0.543015 -0.092483 0.901538 -0.986125 2 True \n", - "1 -1.401457 1.0 0.764608 -0.973136 -1.214477 -1.030017 -0.197350 2 True \n", - "2 -0.844004 0.0 0.277731 0.565246 -0.757885 -1.347978 -1.048337 3 True \n", - "3 -0.943951 1.0 0.671876 -0.150394 -1.508672 -1.237332 -1.764562 3 True \n", - "4 -2.458813 1.0 0.741433 -1.360351 0.382597 -0.090827 -1.391648 2 True \n", + " X0 Z0 Z1 W0 W1 W2 W3 W4 v0 \\\n", + "0 -0.483662 0.0 0.668529 -1.834217 0.899764 -0.027259 -1.162514 2 True \n", + "1 -0.939733 0.0 0.893616 -1.211697 -0.507831 0.524436 2.010319 3 True \n", + "2 -0.601658 0.0 0.134469 -0.567716 -0.923255 0.220309 0.452579 2 False \n", + "3 0.144248 0.0 0.793546 -0.263509 -1.295114 1.636785 -0.414075 2 True \n", + "4 1.044430 0.0 0.152600 -0.561016 -0.493955 1.239707 0.058385 0 True \n", "\n", " y \n", - "0 12.709962 \n", - "1 9.043658 \n", - "2 14.024848 \n", - "3 9.542043 \n", - "4 10.953897 \n", + "0 1.998400 \n", + "1 14.928353 \n", + "2 0.423244 \n", + "3 9.299319 \n", + "4 12.149923 \n", "digraph { U[label=\"Unobserved Confounders\"]; U->y;v0->y;U->v0;W0-> v0; W1-> v0; W2-> v0; W3-> v0; W4-> v0;Z0-> v0; Z1-> v0;W0-> y; W1-> y; W2-> y; W3-> y; W4-> y;X0-> y;}\n", "\n", "\n", @@ -103,7 +103,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We now input a causal graph in the GML graph format (recommended). You can also use the DOT format." + "We now input a causal graph in the GML graph format (recommended). You can also use the DOT format.\n", + "\n", + "To create the causal graph for your dataset, you can use a tool like [DAGitty](http://dagitty.net/dags.html#) that provides a GUI to construct the graph. You can export the graph string that it generates. The graph string is very close to the DOT format: just rename `dag` to `digraph`, remove newlines and add a semicolon after every line, to convert it to the DOT format and input to DoWhy. " ] }, { @@ -206,17 +208,17 @@ "Estimand name: backdoor1 (Default)\n", "Estimand expression:\n", " d \n", - "─────(Expectation(y|X0,W3,W4,W2,W1,W0))\n", + "─────(Expectation(y|W2,W1,W3,W4,W0,X0))\n", "d[v₀] \n", - "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,X0,W3,W4,W2,W1,W0,U) = P(y|v0,X0,W3,W4,W2,W1,W0)\n", + "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W2,W1,W3,W4,W0,X0,U) = P(y|v0,W2,W1,W3,W4,W0,X0)\n", "\n", "### Estimand : 2\n", "Estimand name: backdoor2\n", "Estimand expression:\n", " d \n", - "─────(Expectation(y|W3,W4,W2,W1,W0))\n", + "─────(Expectation(y|W2,W1,W3,W4,W0))\n", "d[v₀] \n", - "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W3,W4,W2,W1,W0,U) = P(y|v0,W3,W4,W2,W1,W0)\n", + "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W2,W1,W3,W4,W0,U) = P(y|v0,W2,W1,W3,W4,W0)\n", "\n", "### Estimand : 3\n", "Estimand name: iv\n", @@ -263,7 +265,7 @@ "output_type": "stream", "text": [ "INFO:dowhy.causal_estimator:INFO: Using Propensity Score Stratification Estimator\n", - "INFO:dowhy.causal_estimator:b: y~v0+X0+W3+W4+W2+W1+W0\n" + "INFO:dowhy.causal_estimator:b: y~v0+W2+W1+W3+W4+W0+X0\n" ] }, { @@ -276,13 +278,13 @@ "Estimand type: nonparametric-ate\n", "\n", "## Realized estimand\n", - "b: y~v0+X0+W3+W4+W2+W1+W0\n", + "b: y~v0+W2+W1+W3+W4+W0+X0\n", "Target units: ate\n", "\n", "## Estimate\n", - "Mean value: 9.902150090407368\n", + "Mean value: 11.627362169966906\n", "\n", - "Causal Estimate is 9.902150090407368\n" + "Causal Estimate is 11.627362169966906\n" ] } ], @@ -310,7 +312,7 @@ "output_type": "stream", "text": [ "INFO:dowhy.causal_estimator:INFO: Using Propensity Score Stratification Estimator\n", - "INFO:dowhy.causal_estimator:b: y~v0+X0+W3+W4+W2+W1+W0\n" + "INFO:dowhy.causal_estimator:b: y~v0+W2+W1+W3+W4+W0+X0\n" ] }, { @@ -323,13 +325,13 @@ "Estimand type: nonparametric-ate\n", "\n", "## Realized estimand\n", - "b: y~v0+X0+W3+W4+W2+W1+W0\n", + "b: y~v0+W2+W1+W3+W4+W0+X0\n", "Target units: atc\n", "\n", "## Estimate\n", - "Mean value: 10.0530141178123\n", + "Mean value: 11.976290956605318\n", "\n", - "Causal Estimate is 10.0530141178123\n" + "Causal Estimate is 11.976290956605318\n" ] } ], @@ -450,7 +452,7 @@ "output_type": "stream", "text": [ "INFO:dowhy.causal_estimator:INFO: Using Propensity Score Stratification Estimator\n", - "INFO:dowhy.causal_estimator:b: y~v0+W2+W1+W3+W4+W0\n" + "INFO:dowhy.causal_estimator:b: y~v0+W2+W4+W1+W0+W3\n" ] }, { @@ -463,13 +465,13 @@ "Estimand type: nonparametric-ate\n", "\n", "## Realized estimand\n", - "b: y~v0+W2+W1+W3+W4+W0\n", + "b: y~v0+W2+W4+W1+W0+W3\n", "Target units: ate\n", "\n", "## Estimate\n", - "Mean value: 9.897148586921062\n", + "Mean value: 11.715836681382548\n", "\n", - "Causal Estimate is 9.897148586921062\n" + "Causal Estimate is 11.715836681382548\n" ] } ], @@ -506,7 +508,7 @@ "output_type": "stream", "text": [ "INFO:dowhy.causal_estimator:INFO: Using Propensity Score Stratification Estimator\n", - "INFO:dowhy.causal_estimator:b: y~v0+W2+W1+W3+W4+W0+w_random\n" + "INFO:dowhy.causal_estimator:b: y~v0+W2+W4+W1+W0+W3+w_random\n" ] }, { @@ -514,8 +516,8 @@ "output_type": "stream", "text": [ "Refute: Add a Random Common Cause\n", - "Estimated effect:9.897148586921062\n", - "New effect:9.911022284476152\n", + "Estimated effect:11.715836681382548\n", + "New effect:11.793706914431112\n", "\n" ] } @@ -542,7 +544,7 @@ "output_type": "stream", "text": [ "INFO:dowhy.causal_estimator:INFO: Using Propensity Score Stratification Estimator\n", - "INFO:dowhy.causal_estimator:b: y~v0+W2+W1+W3+W4+W0\n" + "INFO:dowhy.causal_estimator:b: y~v0+W2+W4+W1+W0+W3\n" ] }, { @@ -550,8 +552,8 @@ "output_type": "stream", "text": [ "Refute: Add an Unobserved Common Cause\n", - "Estimated effect:9.897148586921062\n", - "New effect:8.967526087245673\n", + "Estimated effect:11.715836681382548\n", + "New effect:10.66151529844423\n", "\n" ] } @@ -581,205 +583,205 @@ "text": [ "INFO:dowhy.causal_refuters.placebo_treatment_refuter:Refutation over 100 simulated datasets of permute treatment\n", "INFO:dowhy.causal_estimator:INFO: Using Propensity Score Stratification Estimator\n", - "INFO:dowhy.causal_estimator:b: y~placebo+W2+W1+W3+W4+W0\n", + "INFO:dowhy.causal_estimator:b: y~placebo+W2+W4+W1+W0+W3\n", "INFO:dowhy.causal_estimator:INFO: Using Propensity Score Stratification Estimator\n", - "INFO:dowhy.causal_estimator:b: y~placebo+W2+W1+W3+W4+W0\n", + "INFO:dowhy.causal_estimator:b: y~placebo+W2+W4+W1+W0+W3\n", "INFO:dowhy.causal_estimator:INFO: Using Propensity Score Stratification Estimator\n", - "INFO:dowhy.causal_estimator:b: y~placebo+W2+W1+W3+W4+W0\n", + "INFO:dowhy.causal_estimator:b: y~placebo+W2+W4+W1+W0+W3\n", "INFO:dowhy.causal_estimator:INFO: Using Propensity Score Stratification Estimator\n", - "INFO:dowhy.causal_estimator:b: y~placebo+W2+W1+W3+W4+W0\n", + "INFO:dowhy.causal_estimator:b: y~placebo+W2+W4+W1+W0+W3\n", "INFO:dowhy.causal_estimator:INFO: Using Propensity Score Stratification Estimator\n", - "INFO:dowhy.causal_estimator:b: y~placebo+W2+W1+W3+W4+W0\n", + "INFO:dowhy.causal_estimator:b: y~placebo+W2+W4+W1+W0+W3\n", "INFO:dowhy.causal_estimator:INFO: Using Propensity Score Stratification Estimator\n", - "INFO:dowhy.causal_estimator:b: y~placebo+W2+W1+W3+W4+W0\n", + "INFO:dowhy.causal_estimator:b: y~placebo+W2+W4+W1+W0+W3\n", "INFO:dowhy.causal_estimator:INFO: Using Propensity Score Stratification Estimator\n", - 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"INFO:dowhy.causal_estimator:b: y~placebo+W2+W1+W3+W4+W0\n", + "INFO:dowhy.causal_estimator:b: y~placebo+W2+W4+W1+W0+W3\n", "INFO:dowhy.causal_estimator:INFO: Using Propensity Score Stratification Estimator\n", - "INFO:dowhy.causal_estimator:b: y~placebo+W2+W1+W3+W4+W0\n", + "INFO:dowhy.causal_estimator:b: y~placebo+W2+W4+W1+W0+W3\n", "INFO:dowhy.causal_estimator:INFO: Using Propensity Score Stratification Estimator\n", - "INFO:dowhy.causal_estimator:b: y~placebo+W2+W1+W3+W4+W0\n", + "INFO:dowhy.causal_estimator:b: y~placebo+W2+W4+W1+W0+W3\n", "INFO:dowhy.causal_estimator:INFO: Using Propensity Score Stratification Estimator\n", - "INFO:dowhy.causal_estimator:b: y~placebo+W2+W1+W3+W4+W0\n", + "INFO:dowhy.causal_estimator:b: y~placebo+W2+W4+W1+W0+W3\n", "INFO:dowhy.causal_refuters.placebo_treatment_refuter:Making use of Bootstrap as we have more than 100 examples.\n", " Note: The greater the number of examples, the more accurate are the confidence estimates\n" ] @@ -789,9 +791,9 @@ "output_type": "stream", "text": [ "Refute: Use a Placebo Treatment\n", - "Estimated effect:9.897148586921062\n", - "New effect:0.0029062435605524293\n", - "p value:0.49\n", + "Estimated effect:11.715836681382548\n", + "New effect:0.0031356825084195824\n", + "p value:0.48\n", "\n" ] } @@ -820,205 +822,205 @@ "text": [ "INFO:dowhy.causal_refuters.data_subset_refuter:Refutation over 0.9 simulated datasets of size 18000.0 each\n", "INFO:dowhy.causal_estimator:INFO: Using Propensity Score Stratification Estimator\n", - "INFO:dowhy.causal_estimator:b: y~v0+W2+W1+W3+W4+W0\n", + "INFO:dowhy.causal_estimator:b: y~v0+W2+W4+W1+W0+W3\n", "INFO:dowhy.causal_estimator:INFO: Using Propensity Score Stratification Estimator\n", - "INFO:dowhy.causal_estimator:b: y~v0+W2+W1+W3+W4+W0\n", + "INFO:dowhy.causal_estimator:b: y~v0+W2+W4+W1+W0+W3\n", "INFO:dowhy.causal_estimator:INFO: Using Propensity Score Stratification Estimator\n", - "INFO:dowhy.causal_estimator:b: y~v0+W2+W1+W3+W4+W0\n", + "INFO:dowhy.causal_estimator:b: y~v0+W2+W4+W1+W0+W3\n", "INFO:dowhy.causal_estimator:INFO: Using Propensity Score Stratification Estimator\n", - 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"INFO:dowhy.causal_estimator:b: y~v0+W2+W1+W3+W4+W0\n", + "INFO:dowhy.causal_estimator:b: y~v0+W2+W4+W1+W0+W3\n", "INFO:dowhy.causal_estimator:INFO: Using Propensity Score Stratification Estimator\n", - "INFO:dowhy.causal_estimator:b: y~v0+W2+W1+W3+W4+W0\n", + "INFO:dowhy.causal_estimator:b: y~v0+W2+W4+W1+W0+W3\n", "INFO:dowhy.causal_refuters.data_subset_refuter:Making use of Bootstrap as we have more than 100 examples.\n", " Note: The greater the number of examples, the more accurate are the confidence estimates\n" ] @@ -1028,9 +1030,9 @@ "output_type": "stream", "text": [ "Refute: Use a subset of data\n", - "Estimated effect:9.897148586921062\n", - "New effect:9.918201514500966\n", - "p value:0.33\n", + "Estimated effect:11.715836681382548\n", + "New effect:11.64133310815932\n", + "p value:0.21999999999999997\n", "\n" ] } @@ -1060,205 +1062,205 @@ "text": [ "INFO:dowhy.causal_refuters.data_subset_refuter:Refutation over 0.9 simulated datasets of size 18000.0 each\n", "INFO:dowhy.causal_estimator:INFO: Using Propensity Score Stratification Estimator\n", - 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"INFO:dowhy.causal_estimator:b: y~v0+W2+W1+W3+W4+W0\n", + "INFO:dowhy.causal_estimator:b: y~v0+W2+W4+W1+W0+W3\n", "INFO:dowhy.causal_estimator:INFO: Using Propensity Score Stratification Estimator\n", - "INFO:dowhy.causal_estimator:b: y~v0+W2+W1+W3+W4+W0\n", + "INFO:dowhy.causal_estimator:b: y~v0+W2+W4+W1+W0+W3\n", "INFO:dowhy.causal_estimator:INFO: Using Propensity Score Stratification Estimator\n", - "INFO:dowhy.causal_estimator:b: y~v0+W2+W1+W3+W4+W0\n", + "INFO:dowhy.causal_estimator:b: y~v0+W2+W4+W1+W0+W3\n", "INFO:dowhy.causal_estimator:INFO: Using Propensity Score Stratification Estimator\n", - "INFO:dowhy.causal_estimator:b: y~v0+W2+W1+W3+W4+W0\n", + "INFO:dowhy.causal_estimator:b: y~v0+W2+W4+W1+W0+W3\n", "INFO:dowhy.causal_estimator:INFO: Using Propensity Score Stratification Estimator\n", - "INFO:dowhy.causal_estimator:b: y~v0+W2+W1+W3+W4+W0\n", + "INFO:dowhy.causal_estimator:b: y~v0+W2+W4+W1+W0+W3\n", "INFO:dowhy.causal_estimator:INFO: Using Propensity Score Stratification Estimator\n", - "INFO:dowhy.causal_estimator:b: y~v0+W2+W1+W3+W4+W0\n", + "INFO:dowhy.causal_estimator:b: y~v0+W2+W4+W1+W0+W3\n", "INFO:dowhy.causal_estimator:INFO: Using Propensity Score Stratification Estimator\n", - "INFO:dowhy.causal_estimator:b: y~v0+W2+W1+W3+W4+W0\n", + "INFO:dowhy.causal_estimator:b: y~v0+W2+W4+W1+W0+W3\n", "INFO:dowhy.causal_estimator:INFO: Using Propensity Score Stratification Estimator\n", - "INFO:dowhy.causal_estimator:b: y~v0+W2+W1+W3+W4+W0\n", + "INFO:dowhy.causal_estimator:b: y~v0+W2+W4+W1+W0+W3\n", "INFO:dowhy.causal_estimator:INFO: Using Propensity Score Stratification Estimator\n", - "INFO:dowhy.causal_estimator:b: y~v0+W2+W1+W3+W4+W0\n", + "INFO:dowhy.causal_estimator:b: y~v0+W2+W4+W1+W0+W3\n", "INFO:dowhy.causal_estimator:INFO: Using Propensity Score Stratification Estimator\n", - "INFO:dowhy.causal_estimator:b: y~v0+W2+W1+W3+W4+W0\n", + "INFO:dowhy.causal_estimator:b: y~v0+W2+W4+W1+W0+W3\n", "INFO:dowhy.causal_refuters.data_subset_refuter:Making use of Bootstrap as we have more than 100 examples.\n", " Note: The greater the number of examples, the more accurate are the confidence estimates\n" ] @@ -1268,9 +1270,9 @@ "output_type": "stream", "text": [ "Refute: Use a subset of data\n", - "Estimated effect:9.897148586921062\n", - "New effect:9.919421804260105\n", - "p value:0.35\n", + "Estimated effect:11.715836681382548\n", + "New effect:11.616914320133281\n", + "p value:0.24\n", "\n" ] } diff --git a/dowhy/VERSION b/dowhy/VERSION index 2eb3c4fe4e..4b9fcbec10 100755 --- a/dowhy/VERSION +++ b/dowhy/VERSION @@ -1 +1 @@ -0.5 +0.5.1 diff --git a/setup.py b/setup.py index 12970d8b75..145fd46f16 100755 --- a/setup.py +++ b/setup.py @@ -31,7 +31,7 @@ license='MIT', long_description=long_description, url='https://github.com/microsoft/dowhy', # Optional - download_url='https://github.com/microsoft/dowhy/archive/v0.5.tar.gz', + download_url='https://github.com/microsoft/dowhy/archive/v0.5.1.tar.gz', author='Amit Sharma, Emre Kiciman', classifiers=[ # Optional 'Development Status :: 4 - Beta',