Amit Sharma, Emre Kiciman
arXiv 9 Nov 2020 · Statistics — Methodology · 15 citations (OpenAlex)
arXiv:2011.04216 · PDF · DOI · OpenAlex · Extracted main text
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
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Pearl, Judea (2009) Causality | 0.644 | 2 | 2 | 100% |
| 2 | Athey, Susan, Imbens, Guido W (2017) The state of applied econometrics: Causality and policy evaluation | 0.405 | 1 | 1 | 100% |
| 3 | Chen, Huigang, Harinen, Totte, Lee, Jeong-Yoon, Yung, Mike, Zhao, Zh… (2020) CausalML: Python Package for Causal Machine Learning | 0.405 | 1 | 1 | 100% |
| 4 | Chernozhukov, Victor, Chetverikov, Denis, Demirer, Mert, Duflo, Esth… (2017) Double/debiased/neyman machine learning of treatment effects | 0.405 | 1 | 1 | 100% |
| 5 | (2019) EconML: A Python Package for ML-Based Heterogeneous Treatment Effects Estimation | 0.405 | 1 | 1 | 100% |
| 6 | Imbens, Guido W, Rubin, Donald B (2015) Causal inference in statistics, social, and biomedical sciences | 0.405 | 1 | 1 | 100% |
| 7 | Kıcıman, Emre, Sharma, Amit (2018) Tutorial on Causal Inference and Counterfactual Reasoning self | 0.405 | 1 | 1 | 100% |
| 8 | Paszke, Adam, Gross, Sam, Massa, Francisco, Lerer, Adam, Bradbury, J… (2019) Pytorch: An imperative style, high-performance deep learning library | 0.405 | 1 | 1 | 100% |
| 9 | Abadi, Paul, Chen, Jianmin, Chen, Zhifeng, Davis, Andy, Dean, Jeffre… (2016) Tensorflow: A system for large-scale machine learning | 0.405 | 1 | 1 | 100% |
Showing the top 9 of 9 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Multiply-Robust Causal Change Attribution | 0.405 | 1 | 1 |
| 2 | Valuing an Engagement Surface using a Large Scale Dynamic Causal Model | 0.405 | 1 | 1 |