Code repository & Versions ========================== DoWhy is hosted on GitHub. You can browse the code in a html-friendly format `here <https://github.com/Microsoft/dowhy>`_. v0.7.1: Added Graph refuter. Support for dagitty graphs and external estimators -------------------------------------------------------------------------------------- * Graph refuter with conditional independence tests to check whether data conforms to the assumed causal graph * Better docs for estimators by adding the method-specific parameters directly in its own init method * Support use of custom external estimators * Consistent structure for init_params for dowhy and econml estimators * Add support for Dagitty graphs * Bug fixes for GLM model, causal model with no confounders, and hotel case-study notebook Thank you @EgorKraevTransferwise, @ae-foster, @anusha0409 for your contributions! v0.7: Better Refuters for unobserved confounders and placebo treatment ---------------------------------------------------------------------- * **[Major]** Faster backdoor identification with support for minimal adjustment, maximal adjustment or exhaustive search. More test coverage for identification. * **[Major]** Added new functionality of causal discovery [Experimental]. DoWhy now supports discovery algorithms from external libraries like CDT. `[Example notebook] <https://github.com/microsoft/dowhy/blob/master/docs/source/example_notebooks/dowhy_causal_discovery_example.ipynb>`_ * **[Major]** Implemented ID algorithm for causal identification. [Experimental] * Added friendly text-based interpretation for DoWhy's effect estimate. * Added a new estimation method, distance matching that relies on a distance metrics between inputs. * Heuristics to infer default parameters for refuters. * Inferring default strata automatically for propensity score stratification. * Added support for custom propensity models in propensity-based estimation methods. * Bug fixes for confidence intervals for linear regression. Better version of bootstrap method. * Allow effect estimation without need to refit the model for econml estimators Big thanks to @AndrewC19, @ha2trinh, @siddhanthaldar, and @vojavocni v0.6: Better Refuters for unobserved confounders and placebo treatment ---------------------------------------------------------------------- * **[Major]** Placebo refuter also works for IV methods * **[Major]** Moved matplotlib to an optional dependency. Can be installed using `pip install dowhy[plotting]` * **[Major]** A new method for generating unobserved confounder for refutation * Update to align with EconML's new API * All refuters now support control and treatment values for continuous treatments * Better logging configuration * Dummyoutcomerefuter supports unobserved confounder A big thanks to @arshiaarya, @n8sty, @moprescu and @vojavocni v0.5-beta: 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 <https://github.com/microsoft/dowhy/blob/master/docs/source/example_notebooks/DoWhy-The%20Causal%20Story%20Behind%20Hotel%20Booking%20Cancellations.ipynb>`_ and `membership rewards programs <https://github.com/microsoft/dowhy/blob/master/docs/source/example_notebooks/dowhy_example_effect_of_memberrewards_program.ipynb>`_ * New `notebook <https://github.com/microsoft/dowhy/blob/master/docs/source/example_notebooks/dowhy_multiple_treatments.ipynb>`_ on using DoWhy+EconML for estimating effect of multiple treatments * A `tutorial <https://github.com/microsoft/dowhy/blob/master/docs/source/example_notebooks/tutorial-causalinference-machinelearning-using-dowhy-econml.ipynb>`_ 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 <https://github.com/microsoft/dowhy/blob/master/CONTRIBUTING.md>`_ 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. v0.4-beta: 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! v0.2-alpha: 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! v0.1.1-alpha: First release --------------------------- This is the first release of the library.