- Introducing an updated user guide for navigating the world of causality. The user guide is a great resource to learn about the different causal tasks, which ones may be relevant for you, and how to implement them using DoWhy.
- Causal prediction is the latest task supported by DoWhy! Try out the prediction notebook by @jivatneet
- A new technique for validating causal graphs. Check out the notebook by @eeulig
- New refutation: Overrule for learning boolean rules to describe support of the data/overlap between treatment and control groups in the data. Check out the notebook by @moberst
- Added a new method to estimate intrinsic causal influences for a single sample.
- Refactor of estimator API that allows separate fit and estimate methods
- Several optimizations and speed-ups of GCM methods
- Python 3.11 support and a simpler dependency list
A big thanks to all the contributors. @AlxndrMlk @amit-sharma @andresmor-ms @bloebp @darthtrevino @eeulig @eltociear @emrekiciman @jivatneet @kbattocchi @Klesel @MFreidank @MichaelMarien @moberst @Padarn @petergtz @RoseDeSicilia26 @sgrimbly @vspinu @yoshiakifukushima @Zethson