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DoWhy: An End-to-End Library for Causal Inference · Around

2020

DoWhy: An End-to-End Library for Causal Inference

Sharma, Amit, Kiciman, Emre

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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.

Built on

“CausalML: Python Package for Causal Machine Learning”, 2020
Original
Huigang Chen et al · 2002
Earlier work this paper cites.
“Causality”
Judea Pearl · 2009
Earlier work this paper cites.
“Causal inference in statistics, social, and biomedical sciences”
Guido Imbens and Donald Rubin · 2015
Earlier work this paper cites.

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Then

“Tutorial on Causal Inference and Counterfactual Reasoning”, 2018
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“EconML: A Python Package for ML-Based Heterogeneous Treatment Effects Estimation” Version 0.6, https://github.com/microsoft/EconML, 2019
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