# Model-free causal inference of binary experimental data

arXiv: https://arxiv.org/abs/1705.08526 · HTML: https://mlsanity.com/paper/1705.08526/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 May 2017.

## Built on by (most cited first, 1 of 1)

- [Interpretable (not just posthoc-explainable) medical claims modeling for discharge placement to prevent avoidable all-cause readmissions or death](https://mlsanity.com/paper/2208.12814/) (2022)

## Abstract (arXiv)

For binary experimental data, we discuss randomization-based inferential procedures that do not need to invoke any modeling assumptions. We also introduce methods for likelihood and Bayesian inference based solely on the physical randomization without any hypothetical super population assumptions about the potential outcomes. These estimators have some properties superior to moment-based ones such as only giving estimates in regions of feasible support. Due to the lack of identification of the causal model, we also propose a sensitivity analysis approach which allows for the characterization of the impact of the association between the potential outcomes on statistical inference.