Predictive State Propensity Subclassification (PSPS): A Causal Inference Algorithm for Data-Driven Propensity Score Stratification
Abstract
We introduce Predictive State Propensity Subclassification (PSPS), a novel learning algorithm for causal inference from observational data. PSPS combines propensity and outcome models into one encompassing probabilistic framework, which can be jointly estimated using maximum likelihood or Bayesian inference. The methodology applies to both discrete and continuous treatments and can estimate unit-level and population-level average treatment effects. We describe the neural network architecture and its TensorFlow implementation for likelihood optimization. Finally we demonstrate via large-scale simulations that PSPS outperforms state-of-the-art algorithms – both on bias for average treatment effects (ATEs) and RMSE for unit-level treatment effects (UTEs).
Cite
Text
Kelly et al. "Predictive State Propensity Subclassification (PSPS): A Causal Inference Algorithm for Data-Driven Propensity Score Stratification." Proceedings of the First Conference on Causal Learning and Reasoning, 2022.Markdown
[Kelly et al. "Predictive State Propensity Subclassification (PSPS): A Causal Inference Algorithm for Data-Driven Propensity Score Stratification." Proceedings of the First Conference on Causal Learning and Reasoning, 2022.](https://mlanthology.org/clear/2022/kelly2022clear-predictive/)BibTeX
@inproceedings{kelly2022clear-predictive,
title = {{Predictive State Propensity Subclassification (PSPS): A Causal Inference Algorithm for Data-Driven Propensity Score Stratification}},
author = {Kelly, Joseph and Kong, Jing and Goerg, Georg M.},
booktitle = {Proceedings of the First Conference on Causal Learning and Reasoning},
year = {2022},
pages = {352-372},
volume = {177},
url = {https://mlanthology.org/clear/2022/kelly2022clear-predictive/}
}