DoWhy: An End-to-End Library for Causal Inference
Amit Sharma, Emre Kiciman
TL;DR
DoWhy addresses the gap where causal inference tools emphasize estimation but neglect explicit modeling and validation of assumptions. It proposes a unified four-step API—Model, Identify, Estimate, Refute—built on graphical models and potential outcomes, enabling explicit specification of causal assumptions and automated robustness testing. The library interoperates with EconML and CausalML to expand estimation options and provides multiple refutation methods (placebo tests, bootstrap, sensitivity to unobserved confounding) to assess estimate validity. By making assumptions explicit and providing end-to-end tooling for testing them, DoWhy lowers the barrier for reliable causal analysis and facilitates integration into data science workflows.
Abstract
In addition to efficient statistical estimators of a treatment's effect, successful application of causal inference requires specifying assumptions about the mechanisms underlying observed data and testing whether they are valid, and to what extent. However, most libraries for causal inference focus only on the task of providing powerful statistical estimators. We describe DoWhy, an open-source Python library that is built with causal assumptions as its first-class citizens, based on the formal framework of causal graphs to specify and test causal assumptions. DoWhy presents an API for the four steps common to any causal analysis---1) modeling the data using a causal graph and structural assumptions, 2) identifying whether the desired effect is estimable under the causal model, 3) estimating the effect using statistical estimators, and finally 4) refuting the obtained estimate through robustness checks and sensitivity analyses. In particular, DoWhy implements a number of robustness checks including placebo tests, bootstrap tests, and tests for unoberved confounding. DoWhy is an extensible library that supports interoperability with other implementations, such as EconML and CausalML for the the estimation step. The library is available at https://github.com/microsoft/dowhy