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arXiv Statistics ML

Balance-Guided Forest for Causal Effect Estimation

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arXiv Machine Learning
Sep 16

Splitting the Difference: Interpretable Causal Forests for Treatment Effect Heterogeneity and Bias

The paper introduces a new algorithm that uses decision trees and random forests to estimate individual treatment effects while providing interpretability. It modifies the standard random forest splitting criterion by combining a heterogeneity-focused criterion with a bias-correction criterion, enabling the model to handle observational studies with varying treatment propensities without separately estimating propensity scores. The resulting tree structure directly reveals which features drive treatment effect differences, and simulation studies show the method matches or surpasses existing approaches in prediction accuracy while improving interpretability.

By Nicolas Alexander Ihlo, Merle Behr
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Hugging Face Trending Papers
Jun 2

Tailoring Strictly Proper Scoring Rules for Downstream Tasks: An Application to Causal Inference

Probabilistic models are typically trained using task-agnostic objectives like log-loss, which can lead to significant errors in downstream estimation. This disconnect is especially critical in Inverse Probability Weighting (IPW) for causal inference, where propensity score errors near $0$ and $1$ often lead to high bias and variance.

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arXiv Machine Learning
Jun 9

Transfer learning for causal forest

arXiv:2606. 07693v1 Announce Type: cross Abstract: Transfer learning addresses the challenge of transfering knowledge from one domain to another.

By B\'er\'enice-Alexia Jocteur (ICJ, PSPM), V\'eronique Maume-Deschamps (ICJ, PSPM), Pierre Ribereau (PSPM, ICJ)
fine-tuning
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arXiv Machine Learning
Jul 14

DAG-FM: A Foundation Model for Causal Discovery under Heterogeneous Causal Mechanisms

arXiv:2607. 11510v1 Announce Type: new Abstract: Causal discovery from observational tabular data remains fundamentally challenging, primarily due to the heterogeneity of underlying causal mechanisms and the high-dimensional combinatorial search space of Directed Acyclic Graphs (DAGs).

By Yikang Chen, Zhengkang Guan, Haoyuan Qian, Peng Cui, Yi Yang, Kun Kuang
llmsbenchmarks
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arXiv AI
Oct 5

Mapping and Advancing the Scalability-Accuracy Frontier of Nonlinear Causal Discovery

The paper evaluates four main approaches to scalable nonlinear causal discovery—differentiable, amortized, score‑matching, and combinatorial search—highlighting their distinct accuracy‑runtime trade‑offs. It introduces SPADE, a spline‑based score‑evaluation method that compiles and reuses sufficient statistics, reducing combinatorial search complexity from O(nd³) to O(nd²+d³). Empirical results show SPADE dramatically extends the scalability‑accuracy frontier, solving large‑scale problems with high structural accuracy on both synthetic and real‑world data.

By Hendrik Suhr, Sascha Xu, Jilles Vreeken
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arXiv Machine Learning
Sep 4

Causal Foundation Models

Causal Foundation Models (CFMs) are pretrained neural networks designed to estimate causal quantities—such as the average treatment effect—across new datasets using in‑context learning, eliminating the need for bespoke pipelines or model updates. The paper introduces CFMs, reviews foundational concepts in causal inference and machine learning, and provides practical code examples and Jupyter notebooks to illustrate their application.

By Christopher Stith, Hossein Rahmani, Jesse C. Cresswell
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