# Causal Falling Rule Lists

arXiv: https://arxiv.org/abs/1510.05189 · HTML: https://mlsanity.com/paper/1510.05189/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2015.

## Built on by (most cited first, 3 of 3)

- [Fast Optimization of Weighted Sparse Decision Trees for use in Optimal Treatment Regimes and Optimal Policy Design](https://mlsanity.com/paper/2210.06825/) (2022)
- [Gradient-free Post-hoc Explainability Using Distillation Aided Learnable Approach](https://mlsanity.com/paper/2409.11123/) (2024)
- [Beyond Discriminative Regions: Saliency Maps as Alternatives to CAMs for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2308.11052/) (2023)

## Abstract (arXiv)

A causal falling rule list (CFRL) is a sequence of if-then rules that specifies heterogeneous treatment effects, where (i) the order of rules determines the treatment effect subgroup a subject belongs to, and (ii) the treatment effect decreases monotonically down the list. A given CFRL parameterizes a hierarchical bayesian regression model in which the treatment effects are incorporated as parameters, and assumed constant within model-specific subgroups. We formulate the search for the CFRL best supported by the data as a Bayesian model selection problem, where we perform a search over the space of CFRL models, and approximate the evidence for a given CFRL model using standard variational techniques. We apply CFRL to a census wage dataset to identify subgroups of differing wage inequalities between men and women.