DoWhy for causal inference
DoWhy is a Python library for causal inference and analysis. It is designed to support interoperability with other causal estimation libraries, such as Causal ML and EconML, allowing users to easily combine different methods and approaches in their analysis.
One of the main features of DoWhy is its focus on robustness checks and sensitivity analysis. The library includes a range of methods for evaluating the robustness of causal estimates, such as bootstrapping and placebo tests. These methods help users to ensure that their estimates are reliable and not subject to bias or confounding factors.
In addition to robustness checks, DoWhy also offers an API that follows the common steps involved in causal analysis. These steps include creating a causal model, identifying the effect of interest, estimating the effect using statistical estimators, and validating the estimate through sensitivity analysis and robustness checks.
To create a causal model, users...