# Using Causal Trees to Estimate Personalized Task Difficulty in Post-Stroke Individuals

arXiv: https://arxiv.org/abs/2403.04109 · HTML: https://mlsanity.com/paper/2403.04109/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 10 references that are in the map.
- First posted on arXiv 6 Mar 2024.

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

- [Estimation and Inference of Heterogeneous Treatment Effects using Random Forests](https://mlsanity.com/paper/1510.04342/) (2015)
- [A metric for characterizing the arm nonuse workspace in poststroke individuals using a robot arm](https://mlsanity.com/paper/2401.06974/) (2024)

## Abstract (arXiv)

Adaptive training programs are crucial for recovery post stroke. However, developing programs that automatically adapt depends on quantifying how difficult a task is for a specific individual at a particular stage of their recovery. In this work, we propose a method that automatically generates regions of different task difficulty levels based on an individual's performance. We show that this technique explains the variance in user performance for a reaching task better than previous approaches to estimating task difficulty.