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Powerful refutations and better support for heterogeneous treatment effects
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@amit-sharma amit-sharma released this 11 May 15:57
v0.4
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  • DummyOutcomeRefuter now includes machine learning functions to increase power of the refutation.

    • In addition to generating a random dummy outcome, now you can generate a dummyOutcome that is an arbitrary function of confounders but always independent of treatment, and then test whether the estimated treatment effect is zero. This is inspired by ideas from the T-learner.
    • We also provide default machine learning-based methods to estimate such a dummyOutcome based on confounders. Of course, you can specify any custom ML method.

  • Added a new BootstrapRefuter that simulates the issue of measurement error with confounders. Rather than a simple bootstrap, you can generate bootstrap samples with noise on the values of the confounders and check how sensitive the estimate is.

    • The refuter supports custom selection of the confounders to add noise to.

  • All refuters now provide confidence intervals and a significance value.

  • Better support for heterogeneous effect libraries like EconML and CausalML

    • All CausalML methods can be called directly from DoWhy, in addition to all methods from EconML.
    • [Change to naming scheme for estimators] To achieve a consistent naming scheme for estimators, we suggest to prepend internal dowhy estimators with the string "dowhy". For example, "backdoor.dowhy.propensity_score_matching". Not a breaking change, so you can keep using the old naming scheme too.
    • EconML-specific: Since EconML assumes that effect modifiers are a subset of confounders, a warning is issued if a user specifies effect modifiers outside of confounders and tries to use EconML methods.

  • CI and Standard errors: Added bootstrap-based confidence intervals and standard errors for all methods. For linear regression estimator, also implemented the corresponding parametric forms.

  • Convenience functions for getting confidence intervals, standard errors and conditional treatment effects (CATE), that can be called after fitting the estimator if needed

  • Better coverage for tests. Also, tests are now seeded with a random seed, so more dependable tests.

Thanks to @Tanmay-Kulkarni101 and @Arshiaarya for their contributions!

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