# On the Distinction Between "Conditional Average Treatment Effects" (CATE) and "Individual Treatment Effects" (ITE) Under Ignorability Assumptions

arXiv: https://arxiv.org/abs/2108.04939 · HTML: https://mlsanity.com/paper/2108.04939/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 16 references that are in the map.
- First posted on arXiv 10 Aug 2021.

## Builds on (most cited first, 2 of 2)

- [Balancing Covariates via Propensity Score Weighting](https://mlsanity.com/paper/1609.07494/) (2016)
- [Estimating Individual Treatment Effect in Observational Data Using Random Forest Methods](https://mlsanity.com/paper/1701.05306/) (2017)

## Built on by (most cited first, 9 of 9)

- [Estimating Potential Outcome Distributions with Collaborating Causal Networks](https://mlsanity.com/paper/2110.01664/) (2021)
- [What is Proxy Discrimination?](https://mlsanity.com/paper/2205.05265/) (2022)
- [Language Models as Causal Effect Generators](https://mlsanity.com/paper/2411.08019/) (2024)
- [A New Causal Rule Learning Approach to Interpretable Estimation of Heterogeneous Treatment Effect](https://mlsanity.com/paper/2310.06746/) (2023)
- [SurvCaus : Representation Balancing for Survival Causal Inference](https://mlsanity.com/paper/2203.15672/) (2022)
- [Using representation balancing to learn conditional-average dose responses from clustered data](https://mlsanity.com/paper/2309.03731/) (2023)
- [Bridging the gap: Towards an Expanded Toolkit for AI-driven Decision-Making in the Public Sector](https://mlsanity.com/paper/2310.19091/) (2023)
- [Sample size planning for conditional counterfactual mean estimation with a K-armed randomized experiment](https://mlsanity.com/paper/2403.04039/) (2024)
- [Beyond Interaction Effects: Two Logics for Studying Population Inequalities](https://mlsanity.com/paper/2601.04223/) (2025)

## Abstract (arXiv)

Recent years have seen a swell in methods that focus on estimating "individual treatment effects". These methods are often focused on the estimation of heterogeneous treatment effects under ignorability assumptions. This paper hopes to draw attention to the fact that there is nothing necessarily "individual" about such effects under ignorability assumptions and isolating individual effects may require additional assumptions. Such individual effects, more often than not, are more precisely described as "conditional average treatment effects" and confusion between the two has the potential to hinder advances in personalized and individualized effect estimation.