statanomics

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Here is a one-stop shop of:

1. causal inference models to estimate average treatment effects (ATE/ATET);
2. causal inference models to estimate Heterogeneous Treatment Effects (HTE);
3. diagnostics for assess underlying assumptions needed for causal inference following the Neyman-Rubin's Potential Outcomes model.

The API and syntaxes are centralized, so you can swap one model for another just by changing the functional call!

Installation

available by

pip install --upgrade git+https://github.com/shoepaladin/statanomics#egg=stnomics

causalmodels - ATE

Propensity-score-based models to estimate the average treatment effect and average treatment effect on the treated. While models such as OLS, double robust, and inverse propensity-weighting models are supported.

• Pending functions:
  • quantile regression
  • regressions with discrete outcomes
  • instrumental variable (IV) regression via 2SLS
  • difference-in-difference
• No current plans to support:
  • propensity score matching models
  • kernel and local regression models
  • synthetic control models

causalmodels - HTE

Cross sectional methods to estimate heterogeneous treatment effect (HTE) models.

• Pending support:
  • panel data
  • local linear forests

diagnostics

Various metrics and tests to asses the unconfoundedness and overlap assumptions - following the potential outcomes models. There are no tests for the stable unit treatment value assumption (SUTVA).

• Pending functions:
  • Coefficient stability metric via Oster (2016)
  • Exogeneity sensitivity via Imbens 2003

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(bonus) heterogeneousresiduals

A custom implementation of a heterogeneous treatment effects version for double machine learning, based on The Heterogeneous Residuals model builds on Semenova, Goldman, Chernozhukov, and Taddy (2021).