Causal Inference with Unobserved Confounding

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37 papers11 papers in the last four weeks, up 83% on the four weeks before. 0.1% of all new papers.

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
Learning Granger Causality under Latent Confounding via Intervention-Induced Heterogeneity
Oct 22, 2025Ziyi Zhang, Shaogang Ren, Xiaoning Qian +1Time-Series Causal Discovery Causal Discovery
Texas A&M University · University of Tennessee at Chattanooga · Brookhaven National Laboratory
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
Estimating Long-term Heterogeneous Dose-response Curve: Generalization Bound Leveraging Optimal Transport Weights
Jun 27, 2024Zeqin Yang, Weilin Chen, Ruichu Cai +7Dose-Response Estimation Causal Inference with Unobserved Confounding
School of Computer Science, Guangdong University of Technology · Didi Chuxing · Pazhou Laboratory (Huangpu) +1
A deep causal inference model for interpretable travel behaviour analysis
May 2, 2024Kimia Kamal, Bilal FarooqCausal Inference with Unobserved Confounding Discrete Choice Modeling
Laboratory of Innovations in Transportation (LiTrans), Toronto Metropolitan University, Toronto, Canada
Causal Discovery in Mixtures of Populations
Nov 13, 2023Bijan Mazaheri, Spencer Gordon, Yuval Rabani +1Latent Variable Models Causal Discovery
Thayer School of Engineering, Dartmouth College, Hanover, New Hampshire, USA · Fragment Data Technologies, San Francisco, California, USA · Department of Computer Science, Hebrew Univeristy of Jerusalem, Jerusalem, Israel +1
Causal Inference Under Unmeasured Confounding With Negative Controls: A Minimax Learning Approach
Mar 25, 2021Nathan Kallus, Xiaojie Mao, Masatoshi Uehara +1Causal Inference with Unobserved Confounding Proximal Causal Inference
Cornell University · Tsinghua University

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Uncertainty-aware Causal Decision Making via Effect Bound Decomposition

Md Musfiqur Rahman, Ziwei Jiang, Hilaf Hasson, Murat Kocaoglu

Electrical and Computer Engineering, Purdue University · Computer Science, Johns Hopkins University · Cohesity

Causal Effect Estimation Parameter Identifiability Partial Identification Causal Identifiability Decision-Making under Uncertainty Causal Inference with Unobserved Confounding

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Causal inference from observational data can provide strong evidence for finding the best action in a decision-making scenario without having to perform expensive randomized trials. The causal effect of an action is often not pointwise identifiable even with infinite data due to unobserved confounding factors. Furthermore, having only finitely many samples adds another layer of uncertainty to causal effect estimation. Several existing methods can be used to obtain upper and lower bounds to the causal effect, ranging from symbolic methods to the more recent neural network-based approaches, which implicitly incorporate both sources of uncertainty. However, these methods do not inform whether collecting more samples may or may not help identify the best action from observational data, leaving experts in the dark about their data collection strategies. We address this problem with a novel framework that can distinguish the range of causal effect values that might be eliminated by collecting more samples from the range of values that, with high probability, cannot be eliminated with more observational samples. We show that this partitioning can be obtained by solving max-min and min-max optimization problems. We leverage neural causal models to approximately recover this decomposition in practice. We demonstrate via experiments on synthetic and real-world datasets that our algorithm can determine when collecting more samples will not help determine the best action. Our framework can help practitioners decide when to resort to non-observational studies or seek to measure some of the unmeasured confounders for optimal decision-making.