Causal Inference

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Causal inference asks what would change if a treatment, policy, or other factor were different, rather than merely measuring which variables move together. Researchers use randomized and quasi-experiments, causal graphs with adjustment, instrumental variables and proxies, and semiparametric or machine-learning estimators. Causal discovery from observational, time-series, and network data seeks structures that support these analyses, while counterfactual models help evaluate individual outcomes and policies. Results depend on assumptions—especially about hidden confounding, interference, and transport across populations—so sensitivity analysis, partial-identification bounds, and credible evaluation remain central challenges as generative and foundation models are explored.

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318 papers34 papers in the last four weeks, up 13% on the four weeks before. 0.2% of all new papers.

Causal Effect Estimation with Latent Textual Treatments
Feb 17, 2026Omri Feldman, Amar Venugopal, Jann Spiess +1Causal Effect Estimation Causal Inference
School of Computer Science and Engineering, The Hebrew University of Jerusalem · Department of Economics, Stanford University · Graduate School of Business, Stanford University
CEPAE: Conditional Entropy-Penalized Autoencoders for Time Series Counterfactuals
Feb 17, 2026Tomàs Garriga, Gerard Sanz, Eduard Serrahima de Cambra +1Causal Counterfactual Generation Causal Inference
Novartis · Barcelona Supercomputing Center
Use What You Know: Causal Foundation Models with Partial Graphs
Feb 16, 2026Arik Reuter, Anish Dhir, Cristiana Diaconu +6Causal Foundation Models Causal Discovery
University of Cambridge, Cambridge, United Kingdom · Max Planck Institute for Intelligent Systems, Tübingen, Germany · Gatsby Computational Neuroscience Unit, University College London +5
Bounding Probabilities of Causation with Partial Causal Diagrams
Feb 16, 2026Yuxuan Xie, Ang LiCausal Inference Causal Identifiability
University of Ottawa, Ontario, Canada · Department of Computer Science, Florida State University, FL, U.S.A.
From Confounding to Learning: Dynamic Service Fee Pricing on Third-Party Platforms
Dec 28, 2025Rui Ai, David Simchi-Levi, Feng ZhuCausal Effect Estimation Instrumental Variable Estimation
MIT
Spatial Deconfounder: Interference-Aware Deconfounding for Spatial Causal Inference
Oct 9, 2025Ayush Khot, Miruna Oprescu, Maresa Schröder +2Causal Effect Estimation Causal Inference
University of Illinois at Urbana-Champaign · Cornell University, Cornell Tech · LMU Munich, Munich Center for Machine Learning (MCML) +1
Aligning Language Models with Observational Data: Opportunities and Risks from a Causal Perspective
May 30, 2025Erfan LoghmaniLLM Alignment LLM Fine-Tuning
Foster School of Business University of Washington Seattle, WA 98195
Semiparametric Inference for Counterfactual Regression under Intervention-Driven Shift
Apr 3, 2025Kwangho KimDistribution Shift Robustness Semiparametric Inference
Department of Statistics, Korea University
Quantifying Symptom Causality in Clinical Decision Making: An Exploration Using CausaLM
Mar 25, 2025Alexander Romanus, Mehul Shetty, Connor JordanClinical Decision-Making Causal Interpretability
Causal Effect Estimation under Networked Interference without Networked Unconfoundedness Assumption
Feb 27, 2025Weilin Chen, Ruichu Cai, Jie Qiao +2Causal Effect Estimation Causal Identifiability
School of Computer Science, Guangdong University of Technology, Guangzhou, 510006, China · School of Computer Science, Guangdong University of Technology, Guangzhou, 510006, China and Peng Cheng Laboratory, Shenzhen, China · Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, United Kingdom
SPARC: Scalable Path-Specific Counterfactual Fairness via Causal Conditional Independence
Dec 6, 2024Bowei Tian, Yexiao He, Ziyao Wang +3Debiased ML Algorithmic Fairness
University of Maryland, College Park, MD 20742, USA · Clemson University, Clemson, SC 29634, USA
Identifiability Analysis of Linear ODE Systems with Hidden Confounders
Oct 29, 2024Yuanyuan Wang, Biwei Huang, Wei Huang +2Parameter Identifiability Causal Identifiability
The University of Melbourne · University of California, San Diego
Granger Causality in Extremes
Jul 12, 2024Juraj Bodik, Olivier C. PascheTime-Series Causal Discovery Causal Inference
Faculty of Business and Economics, University of Lausanne, Switzerland · Department of Statistics, UC Berkeley, California, USA · Research Institute for Statistics and Information Science, University of Geneva, Switzerland +1
Causality for Tabular Data Synthesis: A High-Order Structure Causal Benchmark Framework
Jun 12, 2024Zineb Senane, Axel Karlsson, Lele Cao +6Synthetic Benchmark Generation Causal Inference
KTH Royal Institute of Technology · Stockholm, Sweden · King AI Labs, Microsoft +2
Clustered Switchback Designs for Experimentation Under Spatio-temporal Interference
Dec 25, 2023Su Jia, Nathan Kallus, Christina Lee YuCausal Effect Estimation Causal Inference
Cornell University
Epsilon-Identifiability of Causal Quantities
Jan 27, 2023Ang Li, Scott Mueller, Xin Shu +1Causal Identifiability Causal Inference
Cognitive Systems Laboratory, Department of Computer Science, University of California, Los Angeles, Los Angeles, California, USA.
Multiply Robust Causal Mediation Analysis with Continuous Treatments
May 19, 2021Yizhen Xu, AmirEmad Ghassami, Numair Sani +1Causal Effect Estimation Causal Mediation Analysis
Division of Biostatistics, Department of Population Health Sciences, University of Utah, USA · Department of Mathematics and Statistics, Boston University, USA · Department of Computer Science, Johns Hopkins University, USA
Policy design in experiments with unknown interference
Nov 16, 2020Davide Viviano, Jess RudderCausal Effect Estimation Policy Optimization
Department of Economics, Harvard University, 1805 Cambridge St, Cambridge, MA 02138. · Department of Economics, Oregon State University, 1500 SW Jefferson Way, Corvallis, OR 97331.

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Causal Effect Estimation with Latent Textual Treatments

Omri Feldman, Amar Venugopal, Jann Spiess, Amir Feder

School of Computer Science and Engineering, The Hebrew University of Jerusalem · Department of Economics, Stanford University · Graduate School of Business, Stanford University

Causal Effect Estimation Causal Inference Causal Interventions in Language Models

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Understanding the causal effects of text on downstream outcomes is a central task in many applications. Estimating such effects requires researchers to run controlled experiments that systematically vary textual features. While large language models (LLMs) hold promise for generating text, producing and evaluating controlled variation requires more careful attention. In this paper, we present an end-to-end pipeline for the generation and causal estimation of latent textual interventions. Our work first performs hypothesis generation and steering via sparse autoencoders (SAEs), followed by robust causal estimation. Our pipeline addresses both computational and statistical challenges in text-as-treatment experiments. We demonstrate that naive estimation of causal effects suffers from significant bias as text inherently conflates treatment and covariate information. We describe the estimation bias induced in this setting and propose a solution based on covariate residualization. Our empirical results show that our pipeline effectively induces variation in target features and mitigates estimation error, providing a robust foundation for causal effect estimation in text-as-treatment settings.