DoWhy is hosted on GitHub.
You can browse the code in a html-friendly format here.
v0.9: New functional API (preview), faster refutations, and better independence tests for GCMs
December 5 2022
Preview for the new functional API (see notebook). The new API (in experimental stage) allows for a modular use of the different functionalities and includes separate fit and estimate methods for causal estimators. Please leave your feedback here. The old DoWhy API based on CausalModel should work as before. (@andresmor-ms)
- Faster, better sensitivity analyses.
Many refutations now support joblib for parallel processing and show a progress bar (@astoeffelbauer, @yemaedahrav).
Non-linear sensitivity analysis [Chernozhukov, Cinelli, Newey, Sharma & Syrgkanis (2021), example notebook] (@anusha0409)
E-value sensitivity analysis [Ding & Vanderweele (2016) <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4820664/>, example notebook] (@jlgleason)
New API for unit change attribution (@kailashbuki)
New quality option BEST for auto-assignment of causal mechanisms, which uses the optional auto-ML library AutoGluon (@bloebp)
Better conditional independence tests through the causal-learn package (@bloebp)
Algorithms for computing efficient backdoor sets [example notebook] (@esmucler)
Support for estimating controlled direct effect (@amit-sharma)
Support for multi-valued treatments for econml estimators (@EgorKraevTransferwise)
New PyData theme for documentation with new homepage, Getting started guide, revised User Guide and examples page (@petergtz)
A contributing guide and simplified instructions for new contributors (@MichaelMarien)
Streamlined dev environment using Poetry for managing dependencies and project builds (@darthtrevino)
v0.8: GCM support and partial R2-based sensitivity analysis
July 18 2022
A big thanks to @petergtz, @kailashbuki, and @bloebp for the GCM package and @anusha0409 for an implementation of partial R2 sensitivity analysis for linear models.
Graphical Causal Models: SCMs, root-cause analysis, attribution, what-if analysis, and more.
Sensitivity Analysis: Faster, more general partial-R2 based sensitivity analysis for linear models, based on Cinelli & Hazlett (2020).
New docs structure: Updated docs structure including user and contributors’ guide. Check out the docs.
Contributors: @amit-sharma, @anusha0409, @bloebp, @EgorKraevTransferwise, @elikling, @kailashbuki, @itsoum, @MichaelMarien, @petergtz, @ryanrussell
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]
[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
DoWhy can be installed on Conda now!
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
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.
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.
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.