# A tutorial on individualized treatment effect prediction from randomized trials with a binary endpoint

arXiv: https://arxiv.org/abs/2106.05588 · HTML: https://mlsanity.com/paper/2106.05588/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2021.

## Built on by (most cited first, 8 of 8)

- [Causal Machine Learning for Healthcare and Precision Medicine](https://mlsanity.com/paper/2205.11402/) (2022)
- [How to select predictive models for causal inference?](https://mlsanity.com/paper/2302.00370/) (2023)
- [DR-VIDAL -- Doubly Robust Variational Information-theoretic Deep Adversarial Learning for Counterfactual Prediction and Treatment Effect Estimation on Real World Data](https://mlsanity.com/paper/2303.04201/) (2023)
- [Bayesian Pliable Lasso with Horseshoe Prior for Interaction Effects in GLMs with Missing Responses](https://mlsanity.com/paper/2509.07501/) (2025)
- [A Partial Initialization Strategy to Mitigate the Overfitting Problem in CATE Estimation with Hidden Confounding](https://mlsanity.com/paper/2501.08888/) (2025)
- [Outcome prediction and individualized treatment effect estimation in patients with large vessel occlusion stroke](https://mlsanity.com/paper/2507.03046/) (2025)
- [Advantages and limitations in the use of transfer learning for individual treatment effects in causal machine learning](https://mlsanity.com/paper/2512.16489/) (2025)
- [Estimating Individualized Treatment Effects in Acute Ischemic Stroke with Causal Transformation Models (TRAM-DAG): A Multi-Centre Observational Study with External RCT Validation](https://mlsanity.com/paper/2606.12623/) (2026)

## Abstract (arXiv)

Randomized trials typically estimate average relative treatment effects, but decisions on the benefit of a treatment are possibly better informed by more individualized predictions of the absolute treatment effect. In case of a binary outcome, these predictions of absolute individualized treatment effect require knowledge of the individual's risk without treatment and incorporation of a possibly differential treatment effect (i.e. varying with patient characteristics). In this paper we lay out the causal structure of individualized treatment effect in terms of potential outcomes and describe the required assumptions that underlie a causal interpretation of its prediction. Subsequently, we describe regression models and model estimation techniques that can be used to move from average to more individualized treatment effect predictions. We focus mainly on logistic regression-based methods that are both well-known and naturally provide the required probabilistic estimates. We incorporate key components from both causal inference and prediction research to arrive at individualized treatment effect predictions. While the separate components are well known, their successful amalgamation is very much an ongoing field of research. We cut the problem down to its essentials in the setting of a randomized trial, discuss the importance of a clear definition of the estimand of interest, provide insight into the required assumptions, and give guidance with respect to modeling and estimation options. Simulated data illustrates the potential of different modeling options across scenarios that vary both average treatment effect and treatment effect heterogeneity. Two applied examples illustrate individualized treatment effect prediction in randomized trial data.