Bug fixes update

• Added an optimized version for identify_effect
• Fixed a bug for direct and indirect effects computation
• More test coverage: Notebooks are also under automatic tests
• updated conditional-effects-notebook to support the latest EconML version
• EconML metalearners now have the expected behavior: accept both common_causes and effect_modifiers
• Fixed some bugs in refuter tests

Enhanced documentation and support for causal mediation

Installation

• DoWhy can be installed on Conda now!

Code

• Support for identification by mediation formula
• Support for the front-door criterion
• Linear estimation methods for mediation
• Generalized backdoor criterion implementation using paths and d-separation
• Added GLM estimators, including logistic regression
• New API for interpreting causal models, estimates and refuters. First interpreter by @ErikHambardzumyan visualizes
  how the distribution of confounder changes
• Friendlier error messages for propensity score stratification estimator when there is not enough data in a bin.
• Enhancements to the dummy outcome refuter with machine learned components--now can simulate non-zero effects too. Ready for alpha testing

Docs

• New case studies using DoWhy on hotel booking cancellations and membership rewards programs.
• New notebook on using DoWhy+EconML for estimating effect of multiple treatments
• A tutorial on causal inference using dowhy and econml
• Better organization of docs and notebooks on the documentation website (https://microsoft.github.io/dowhy/)

Community

• Created a contributors page with guidelines for contributing
• Added allcontributors bot so that new contributors can added just after their pull requests are merged

A big thanks to @Tanmay-Kulkarni101, @ErikHambardzumyan, @Sid-darthvader for their contributions.

Powerful refutations and better support for heterogeneous treatment effects

• 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!

CATE estimation and integration with EconML

This release includes many major updates:

• (BREAKING CHANGE) The CausalModel import is now simpler: "from dowhy import CausalModel"
• Multivariate treatments are now supported.
• Conditional Average Treatment Effects (CATE) can be estimated for any subset of the data. Includes integration with EconML--any method from EconML can be called using DoWhy through the estimate_effect method (see example notebook).
• Other than CATE, specific target estimands like ATT and ATC are also supported for many of the estimation methods.
• For reproducibility, you can specify a random seed for all refutation methods.
• Multiple bug fixes and updates to the documentation.

Includes contributions from @j-chou, @ktmud, @jrfiedler, @shounak112358, @Lnk2past. Thank you all!

First release

This release implements the four steps of causal inference: model, identify, estimate and refute. It also includes a pandas.DataFrame extension for causal inference and the do-sampler.