# Prediction meets causal inference: the role of treatment in clinical prediction models

arXiv: https://arxiv.org/abs/2004.06998 · HTML: https://mlsanity.com/paper/2004.06998/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Apr 2020.

## Built on by (most cited first, 7 of 7)

- [Causal machine learning for predicting treatment outcomes](https://mlsanity.com/paper/2410.08770/) (2024)
- [From algorithms to action: improving patient care requires causality](https://mlsanity.com/paper/2209.07397/) (2022)
- [Doubly Robust Counterfactual Classification](https://mlsanity.com/paper/2301.06199/) (2023)
- [Uncertainty-Aware Optimal Treatment Selection for Clinical Time Series](https://mlsanity.com/paper/2410.08816/) (2024)
- [Boosting the interpretability of clinical risk scores with intervention predictions](https://mlsanity.com/paper/2207.02941/) (2022)
- [Semiparametric Inference for Counterfactual Regression under Intervention-Driven Shift](https://mlsanity.com/paper/2504.02694/) (2025)
- [Fixing a Model That Learned Worse Cancer Means Lower Risk: Monotonic Constraints in Bladder Cancer Recurrence Prediction](https://mlsanity.com/paper/2610.00858/) (2026)

## Abstract (arXiv)

In this paper we study approaches for dealing with treatment when developing a clinical prediction model. Analogous to the estimand framework recently proposed by the European Medicines Agency for clinical trials, we propose a `predictimand' framework of different questions that may be of interest when predicting risk in relation to treatment started after baseline. We provide a formal definition of the estimands matching these questions, give examples of settings in which each is useful and discuss appropriate estimators including their assumptions. We illustrate the impact of the predictimand choice in a dataset of patients with end-stage kidney disease. We argue that clearly defining the estimand is equally important in prediction research as in causal inference.