# Partial Identification of the Average Treatment Effect with Stochastic Counterfactuals and Discordant Twins

arXiv: https://arxiv.org/abs/2407.19057 · HTML: https://mlsanity.com/paper/2407.19057/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jul 2024.

## Built on by (most cited first, 2 of 2)

- [Branch and Bound to Assess Stability of Regression Coefficients in Uncertain Models](https://mlsanity.com/paper/2408.09634/) (2024)
- [Sensitivity Analysis of the Consistency Assumption](https://mlsanity.com/paper/2512.21379/) (2025)

## Abstract (arXiv)

We develop a novel approach to partially identify causal estimands, such as the average treatment effect (ATE), from observational data. To better satisfy the stable unit treatment value assumption (SUTVA) we utilize stochastic counterfactuals within a propensity-prognosis model of the data generating process. For more precise identification we utilize knowledge of discordant twin outcomes as evidence for randomness in the data generating process. Our approach culminates with a constrained optimization problem; the solution gives upper and lower bounds for the ATE. We demonstrate the applicability of our introduced methodology with three example applications.