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py-why / dowhy

DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.

Home Page:https://www.pywhy.org/dowhy

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Remove use of CausalModel from test files and notebooks

rahulbshrestha opened this issue · comments

commented

Is your feature request related to a problem? Please describe.
CausalModel is deprecated and should be removed from tutorials and test cases.

Describe the solution you'd like
Replace it with the new API for effect estimation (https://github.com/py-why/dowhy/wiki/API-proposal-for-v1). The files that still use CausalModel are listed below. I will create a PR to replace in some of those files, but will likely not cover all, so this should serve as a record of the files that still needed to be changed.

Test files

  • /tests/causal_refuters/test_assess_overlap.py
  • /tests/causal_refuters/test_add_unobserved_common_cause.py
  • tests/causal_refuters
  • tests/causal_identifiers/test_optimize_backdoor.py
  • tests/causal_identifiers/test_frontdoor_identifier.py
  • tests/causal_estimators/test_two_stage_regression_estimator.py
  • tests/causal_estimators/test_econml_estimator.py
  • tests/causal_estimators/test_causalml_estimator.py
  • test_causal_model.py
  • test_causal_graph.py

Notebooks

  • docs/source/example_notebooks/tutorial-causalinference-machinelearning-using-dowhy-econml.ipynb
  • docs/source/example_notebooks/sensitivity_analysis_testing.ipynb
  • docs/source/example_notebooks/sensitivity_analysis_nonparametric_estimators.ipynb
  • docs/source/example_notebooks/load_graph_example.ipynb
  • docs/source/example_notebooks/identifying_effects_using_id_algorithm.ipynb
  • docs/source/example_notebooks/graph_conditional_independence_refuter.ipynb
  • docs/source/example_notebooks/DoWhy-The Causal Story Behind Hotel Booking Cancellations.ipynb
  • docs/source/example_notebooks/dowhy-simple-iv-example.ipynb
  • docs/source/example_notebooks/dowhy-conditional-treatment-effects.ipynb
  • docs/source/example_notebooks/dowhy_twins_example.ipynb
  • docs/source/example_notebooks/dowhy_simple_example.ipynb
  • docs/source/example_notebooks/dowhy_refuter_notebook.ipynb
  • docs/source/example_notebooks/dowhy_refuter_assess_overlap.ipynb
  • docs/source/example_notebooks/dowhy_refutation_testing.ipynb
  • docs/source/example_notebooks/dowhy_ranking_methods.ipynb
  • docs/source/example_notebooks/dowhy_optimize_backdoor_example.ipynb
  • docs/source/example_notebooks/dowhy_multiple_treatments.ipynb
  • docs/source/example_notebooks/dowhy_mediation_analysis.ipynb
  • docs/source/example_notebooks/dowhy_lalonde_example.ipynb
  • docs/source/example_notebooks/dowhy_interpreter.ipynb
  • docs/source/example_notebooks/dowhy_ihdp_data_example.ipynb
  • docs/source/example_notebooks/dowhy_functional_api.ipynb
  • docs/source/example_notebooks/dowhy_example_effect_of_memberrewards_program.ipynb
  • docs/source/example_notebooks/dowhy_estimation_methods.ipynb
  • docs/source/example_notebooks/dowhy_demo_dummy_outcome_refuter.ipynb
  • docs/source/example_notebooks/dowhy_confounder_example.ipynb
  • docs/source/example_notebooks/dowhy_causal_discovery_example.ipynb
  • docs/source/example_notebooks/do_sampler_demo.ipynb

cc: @bloebp