Causal Effect Estimation

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Causal effect estimation asks what would happen to outcomes if people, policies, or systems received different treatments, including average, subgroup-specific, and dose-response effects. Researchers use randomized trials and quasi-experiments alongside observational methods such as matching, propensity weighting, g-computation, instrumental variables, synthetic controls, and doubly robust or debiased machine learning. Current work extends these tools to longitudinal decisions, networks with spillovers, multiple or continuous treatments, and effects on entire outcome distributions, while improving uncertainty estimates and experimental design. A central challenge is that flexible prediction cannot by itself resolve unmeasured confounding, weak overlap, or violated transport assumptions, so credible estimates depend on identification assumptions and careful validation.

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266 papers35 papers in the last four weeks, up 35% on the four weeks before. 0.2% of all new papers.

Topological Causal Effects
Mar 2, 2026Kwangho Kim, Hajin LeeCausal Effect Estimation Topological Data Analysis
Department of Statistics, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Korea
Causal Effects with Unobserved Unit Types in Interacting Human-AI Systems
Mar 2, 2026William Overman, Sadegh Shirani, Mohsen BayatiCausal Effect Estimation Causal Inference
Graduate School of Business, Stanford University
Robust Weighted Triangulation of Causal Effects Under Model Uncertainty
Mar 1, 2026Rohit Bhattacharya, Ina Ocelli, Ted WestlingSemiparametric Inference Causal Effect Estimation
Dept. of Computer Science, Williams College · Dept. of Mathematics and Statistics, University of Massachusetts Amherst
Identifying and Characterising Response in Clinical Trials: Development and Validation of a Machine Learning Approach in Colorectal Cancer
Feb 28, 2026Adam Marcus, Paul AgapowCancer Drug Response Prediction Individualized Treatment Effect Estimation
Department of Computing Imperial College London London, SW7 2AZ, UK · Oncology R&D ML&AI AstraZeneca Cambridge, CB2 1PQ, UK
Coarsening Bias from Variable Discretization in Causal Functionals
Feb 25, 2026Xiaxian Ou, Razieh NabiCausal Effect Estimation
Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, U.S.A.
CausalReasoningBenchmark: A Real-World Benchmark for Disentangled Evaluation of Causal Identification and Estimation
Feb 24, 2026Ayush Sawarni, Jiyuan Tan, Vasilis SyrgkanisCausal Effect Estimation Benchmark Design
Stanford University
Detecting and Mitigating Group Bias in Heterogeneous Treatment Effects
Feb 23, 2026Joel Persson, Jurriën Bakker, Dennis Bohle +2Shrinkage Estimation Causal Effect Estimation
Spotify · Munich Center for Machine Learning & LMU Munich · ETH Zurich
Orthogonal Uplift Learning with Permutation-Invariant Representations for Combinatorial Treatments
Feb 23, 2026Xinyan Su, Jiacan Gao, Mingyuan Ma +6Uplift Modeling Causal Effect Estimation
Didi Chuxing, Beijing, China · School of Statistics, East China Normal University, Shanghai, China · School of Mathematics and Statistics, Beijing Jiaotong University, Beijing, China +2
genriesz: A Python Package for Automatic Debiased Machine Learning with Generalized Riesz Regression
Feb 19, 2026Masahiro KatoDebiased ML Causal Effect Estimation
Data Analytics Department, Mizuho-DL Financial Technology, Co., Ltd.
The Impact of Formations on Football Matches Using Double Machine Learning. Is it worth parking the bus?
Feb 18, 2026Genís Ruiz-Menárguez, Llorenç BadiellaCausal Effect Estimation Double ML
Departament de Matemàtiques, Universitat Autònoma de Barcelona, Spain
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
Jointly Optimizing Debiased CTR and Uplift for Coupons Marketing: A Unified Causal Framework
Feb 13, 2026Siyun Yang, Shixiao Yang, Jian Wang +6Causal Effect Estimation Online Advertising
Kuaishou Technology Beijing, China · Beijing Institute of Technology Beijing, China · Independent Researcher Beijing, China
Uncertainty-aware Causal Decision Making via Effect Bound Decomposition
Jan 30, 2026Md Musfiqur Rahman, Ziwei Jiang, Hilaf Hasson +1Causal Effect Estimation Parameter Identifiability
Electrical and Computer Engineering, Purdue University · Computer Science, Johns Hopkins University · Cohesity
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
Causal inference and model explainability analysis of a real world sales dataset
Dec 14, 2025Pranav Gupta, Nithin SurendranCausal Effect Estimation Interpretable ML
Lowe’s Companies Inc, Mooresville, NC, USA
Reliable Selection of Heterogeneous Treatment Effect Estimators
Nov 23, 2025Jiayi Guo, Zijun GaoCausal Effect Estimation Conditional Average Treatment Effect Estimation
Peking University · Marshall School of Business, University of Southern California
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
EconCausal: A Context-Aware Economic Reasoning Benchmark for Large Language Models
Oct 8, 2025Donggyu Lee, Hyeok Yun, Meeyoung Cha +3Causal Reasoning in Language Models LLM Evaluation
Graduate School of Data Science, KAIST · College of Business, KAIST · Data Science for Humanity Group, MPI-SP +2
PAC-Causal Learnability: From Identification to Finite-Sample Guarantees
Jul 25, 2025Mian Wei, Somesh Jha, David PageCausal Effect Estimation Causal Identifiability
Duke University · University of Wisconsin-Madison
ACTIVA: Amortized Causal Effect Estimation via Variational Autoencoders
Mar 3, 2025Andreas Sauter, Saber Salehkaleybar, Frank van Harmelen +2Causal Effect Estimation Causal Intervention
Learning and Reasoning Group, Vrije Universiteit Amsterdam · Leiden Institute of Advanced Computer Science (LIACS), Leiden University · Informatics Institute & Institute for Logic, Language and Computation, University of Amsterdam
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
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.
Synthetic Blips: Generalizing Synthetic Controls for Dynamic Treatment Effects
Oct 20, 2022Anish Agarwal, Sukjin Han, Dwaipayan Saha +2Causal Effect Estimation Sequential Decision Making
Columbia University · University of Bristol · Stanford University +1
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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Topological Causal Effects

Kwangho Kim, Hajin Lee

Department of Statistics, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Korea

Causal Effect Estimation Topological Data Analysis

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Estimating causal effects is particularly challenging when outcomes arise in complex, non-Euclidean spaces, where conventional methods often fail to capture meaningful structural variation. We develop a framework for topological causal inference that defines treatment effects through differences in the topological structure of potential outcomes, summarized by power-weighted silhouette functions of persistence diagrams. We develop an efficient, doubly robust estimator in a fully nonparametric model, establish functional weak convergence, and construct a formal test of the null hypothesis of no topological effect. Empirical studies illustrate that the proposed method reliably quantifies topological treatment effects across diverse complex outcome types.