# Sensitivity Analysis of the Consistency Assumption

arXiv: https://arxiv.org/abs/2512.21379 · HTML: https://mlsanity.com/paper/2512.21379/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 41 references that are in the map.
- First posted on arXiv 24 Dec 2025.

## Builds on (most cited first, 4 of 4)

- [Maximum Entropy Estimation of Heterogeneous Causal Effects](https://mlsanity.com/paper/2407.08862/) (2024)
- [Partial Identification of the Average Treatment Effect with Stochastic Counterfactuals and Discordant Twins](https://mlsanity.com/paper/2407.19057/) (2024)
- [Causal Inference with Two Versions of Treatment](https://mlsanity.com/paper/1705.03918/) (2017)
- [Observational Causality Testing](https://mlsanity.com/paper/2309.02621/) (2023)

## Abstract (arXiv)

Sensitivity analysis informs causal inference by assessing the sensitivity of conclusions to departures from assumptions. The consistency assumption states that there are no hidden versions of treatment and that the outcome arising naturally equals the outcome arising from intervention. When reasoning about the possibility of consistency violations, it can be helpful to distinguish between covariates and versions of treatment. In the context of surgery, for example, genomic variables are covariates and the skill of a particular surgeon is a version of treatment. There may be hidden versions of treatment, and this paper addresses that concern with a new kind of sensitivity analysis. Whereas many methods for sensitivity analysis are focused on confounding by unmeasured covariates, the methodology of this paper is focused on confounding by hidden versions of treatment. In this paper, new mathematical notation is introduced to support the novel method, and example applications are described.