Latest Causal Inference Research Papers

The newest Causal Inference papers from across the field — arXiv, NeurIPS, CVPR, Nature, and more — refreshed daily and ranked by relevance. Distill AI tracks Causal Inference so you don’t have to: get the standout work delivered to your inbox every morning, with 2-sentence summaries and the option to chat with any paper.

Recent papers

• Express Language Modeling
  Albert Gong, Annabelle Michael Carrell, Raaz Dwivedi, Lester Mackey · arXiv · Jun 9, 2026

  We introduce a new tool, Express, for converting a non-causal attention approximation into a causal approximation with matching approximation guarantees. When combined with the state-of-the-art Thinformer approximation, Express improves upo…

• A Mean-Field Analysis of Multi-Head Self-Attention under Cross-Entropy Training
  Cheng Huan, Hongfwei Yuan · arXiv · Jun 9, 2026

  This paper develops a mean-field theory for a simplified single-layer causal multi-head self-attention model trained by cross-entropy minimization. Each attention head is treated as a particle in parameter space, and the empirical law of th…

• Estimate Collapsibility of Causal Effects in Completed Partial DAGs via Strong d-Convex Hulls
  Yuxin Deng, Yi Sun, Zhiming Li, Huaxiong Liu · arXiv · Jun 8, 2026

  This paper proposes a collapsible method for estimating causal effects that maintains the estimator's consistency before and after marginalization over some variables in completed partially directed acyclic graphs (CPDAGs). We first introdu…

• Querying Counterfactuals on Tissue Graphs with Supervised Disentanglement
  Abdul Moeed, Stefan Schrod, Martin Rohbeck, Marc Jan Bonder et al. · arXiv · Jun 7, 2026

  \textit{Tissue graph counterfactuals} ask how a cell's expression would change under altered spatial neighbor contexts. Such queries are central to predicting cell behavior in tissues, but lack a unified definition, with existing methods ta…

• Automatic, Debiased, and Invariant Counterfactual Generation under General Interventions
  Raphael C Kim, Jingsen Zhu, Ramin Zabih, Michele Santacatterina · arXiv · Jun 5, 2026

  Generative models for counterfactual outcomes have great potential to support decision-making under complex interventions, but existing approaches are limited by unstable estimation, poor generalization across environments, and bias from nu…

• Causal Atlases from Entropic Inference: Bayesian Networks beyond Optimal DAGs
  Hazhir Aliahmadi, Irina Babayan, Greg van Anders · arXiv · Jun 4, 2026

  Data-driven causal relationship identification is pertinent to advancing understanding of complex systems both within and beyond science. Bayesian networks offer a probabilistic method for modelling generic causal relationships via directed…

• Discrete Causal Representations from Heterogeneous Domains: A Bayesian Approach with Social Survey Applications
  Ankur Garg, Michael Stettler, Aaron Schein, Julius von Kügelgen · arXiv · Jun 4, 2026

  Causal representation learning aims to infer the high-level latent causal concepts that give rise to observed low-level measurements. This is particularly relevant for heterogeneous data from different environments or domains since distribu…

• EML-CD: Causal Mechanism Recovery via EML Symbolic Trees in Structure Learning
  Sota Asanuma · arXiv · Jun 4, 2026

  Neural network (NN)-based nonlinear causal discovery methods recover DAG structure but leave each causal mechanism as a black box. Waxman et al. argued that extracting causal mechanisms from NN weights is ill-posed. We propose EML-CD, a fra…

• Improved Guarantees for Heterogeneous Treatment-Effect Estimation via Matrix Completion
  Anay Mehrotra, Phuc Tran, Van H. Vu, Manolis Zampetakis · arXiv · May 28, 2026

  A central goal of modern causal inference is estimating heterogeneous treatment effects to answer questions like "how does an intervention affect each unit," rather than only on average. We study this problem with panel-data where we observ…

• Counterfactually Fair Regression via Optimal Transport
  M. Generali Lince, S. Gaucher, J-J. Vie, P. Loiseau · arXiv · May 27, 2026

  We consider the problem of learning a counterfactually fair regressor. We adopt a causal uncertainty view in which counterfactual fairness is defined with resampled noise. We focus on obtaining theoretical fairness guarantees for a new post…

• Geometry Adaptive Counterfactual Distribution Learning with Diffusion-Guided Smoothing
  Kwangho Kim · arXiv · May 25, 2026

  We study counterfactual distribution learning for high-dimensional outcomes whose counterfactual law may concentrate near lower-dimensional structure. Standard isotropic smoothing treats all ambient directions equally, leading to unfavorabl…

• Causal Discovery in Structural VAR Models Under Equal Noise Variance
  SeyedSina Seyedi HasanAbadi, Fahimeh Arab, Erfan Nozari, AmirEmad Ghassami · arXiv · May 21, 2026

  Causal discovery from multivariate time series is challenging when causal effects may occur both across time and within the same sampling interval. This issue is especially important in applications such as neuroscience, where the sampling …

• Stable Causal Discovery via Directed Acyclic Graph Aggregation
  Yunan Wu, Yue Wang, Chunlin Li, Chenglong Ye · arXiv · May 18, 2026

  Directed Acyclic Graphs (DAGs) are central to uncovering causal structure in complex systems, yet learning a single DAG from data is often challenging: model uncertainty, finite samples, and a combinatorially large search space frequently y…

• Adaptive Experimentation for Censored Survival Outcomes
  Yuxin Wang, Dennis Frauen, Jonas Schweisthal, Maresa Schröder et al. · arXiv · May 18, 2026

  Adaptive experimentation enables efficient estimation of causal effects, but existing methods are not designed for survival data with censoring, where event times are only partially observed (e.g., overall survival in cancer trials but with…

• A Recursive Decomposition Framework for Causal Structure Learning in the Presence of Latent Variables
  Zheng Li, Feng Xie, Shenglan Nie, Xichen Guo et al. · arXiv · May 11, 2026

  Constraint-based causal discovery is widely used for learning causal structures, but heavy reliance on conditional independence (CI) testing makes it computationally expensive in high-dimensional settings. To mitigate this limitation, many …

• Amortizing Causal Sensitivity Analysis via Prior Data-Fitted Networks
  Emil Javurek, Dennis Frauen, Marie Brockschmidt, Jonas Schweisthal et al. · arXiv · May 11, 2026

  Causal sensitivity analysis aims to provide bounds for causal effect estimates in the presence of unobserved confounding. However, existing methods for causal sensitivity analysis are per-instance procedures, meaning that changes to the dat…

• Real vs. Semi-Simulated: Rethinking Evaluation for Treatment Effect Estimation
  George Panagopoulos · arXiv · May 11, 2026

  Estimating heterogeneous treatment effects with machine learning has attracted substantial attention in both academic research and industrial practice. However, the two communities often evaluate models under markedly different conditions. …

• Semiparametric Efficient Test for Interpretable Distributional Treatment Effects
  Houssam Zenati, Arthur Gretton · arXiv · May 8, 2026

  Distributional treatment effects can be invisible to means: a treatment may preserve average outcomes while changing tails, modes, dispersion, or rare-event probabilities. Kernel tests can detect discrepancies between interventional outcome…

• Debiased Counterfactual Generation via Flow Matching from Observations
  Hugh Dance, Johnny Xi, Peter Orbanz, Benjamin Bloem-Reddy · arXiv · May 8, 2026

  Estimating counterfactual distributions under interventions is central to treatment risk assessment and counterfactual generation tasks. Existing approaches model the counterfactual distribution as a standalone generative target, without ex…

• Revisiting Transformer Layer Parameterization Through Causal Energy Minimization
  Jin Xu, Camille Couturier, Victor Rühle, Saravan Rajmohan et al. · arXiv · May 8, 2026

  Transformer blocks typically combine multi-head attention (MHA) for token mixing with gated MLPs for token-wise feature transformation, yet many choices in their parameterization remain largely empirical. We introduce Causal Energy Minimiza…

• Dynamic Treatment on Networks
  Bengusu Nar, Jiguang Li, Veronika Ročková, Panos Toulis · arXiv · May 7, 2026

  In networks, effective dynamic treatment allocation requires deciding both whom to treat and also when, so as to amplify policy impact through spillovers. An early intervention at a well-connected node can trigger cascades that change which…

• Risk-Controlled Post-Processing of Decision Policies
  Sunay Joshi, Tao Wang, Hamed Hassani, Edgar Dobriban · arXiv · May 7, 2026

  Predictive models are often deployed through existing decision policies that stakeholders are reluctant to change unless a risk constraint requires intervention. We study risk-controlled post-processing: given a deterministic baseline polic…

• TinyBayes: Closed-Form Bayesian Inference via Jacobi Prior for Real-Time Image Classification on Edge Devices
  Shouvik Sardar, Sourish Das · arXiv · May 7, 2026

  Cocoa (Theobroma cacao) is a critical cash crop for millions of smallholder farmers in West Africa, where Cocoa Swollen Shoot Virus Disease (CSSVD) and anthracnose cause devastating yield losses. Automated disease detection from leaf images…

• When Does Gene Regulatory Network Inference Break? A Controlled Diagnostic Study of Causal and Correlational Methods on Single-Cell Data
  Miguel Fernandez-de-Retana, Ruben Sanchez-Corcuera, Unai Zulaika, Aritz Bilbao-Jayo et al. · arXiv · May 6, 2026

  Despite theoretical advantages, causal methods for Gene Regulatory Network (GRN) inference from single-cell RNA-seq data consistently fail to match or outperform correlation-based baselines in many realistic benchmarks, a persistent puzzle …

• PAIR-CI: Calibrated Conditional Independence Testing for Causal Discovery with Incomplete Data
  Thomas S. Robinson, Ranjit Lall · arXiv · May 6, 2026

  The standard constraint-based paradigm for causal discovery with incomplete data -- impute first, test second -- is frequently miscalibrated: any consistent conditional independence (CI) test rejects a true null with probability approaching…

• Can Causal Discovery Algorithms Help in Generating Legal Arguments?
  Soham Wasmatkar, Subinay Adhikary, Rakshit Rohan, Shouvik Kumar Guha et al. · arXiv · May 4, 2026

  In 2011, Judea Pearl received the Turing Award, considered the Nobel Prize in Computing, for fundamental contributions to artificial intelligence through the development of a calculus for probabilistic and causal reasoning. It includes pion…

• The Causal Description Gap: Information-Theoretic Separations Across Pearl's Hierarchy
  Seyed Morteza Emadi · arXiv · May 4, 2026

  Pearl's causal hierarchy shows that observational, interventional, and counterfactual queries are qualitatively distinct. We ask a quantitative version of this question: how many additional bits are needed to specify higher-rung causal answ…

• M-CaStLe: Uncovering Local Causal Structures in Multivariate Space-Time Gridded Data
  J. Jake Nichol, Michael Weylandt, G. Matthew Fricke, Jhayron Perez-Carrasquilla et al. · arXiv · May 1, 2026

  Causal graph discovery for space-time systems is challenging in high-dimensional gridded data, which often has many more grid cells than temporal observations per cell. The Causal Space-Time Stencil Learning (CaStLe) meta-algorithm was deve…

• Bayesian X-Learner: Calibrated Posterior Inference for Heterogeneous Treatment Effects under Heavy-Tailed Outcomes
  Eichi Uehara · arXiv · Apr 30, 2026

  Conditional Average Treatment Effect (CATE) estimation in practice demands three properties simultaneously: heterogeneous effects $τ(x)$, calibrated uncertainty over them, and robustness to the heavy tails that contaminate real outcome data…

• A Novel Computational Framework for Causal Inference: Tree-Based Discretization with ILP-Based Matching
  Tianyu Yang, Md. Noor-E-Alam · arXiv · Apr 30, 2026

  Causal inference is essential for data-driven decision-making, as it aims to uncover causal relationships from observational data. However, identifying causality remains challenging due to the potential for confounding and the distinction b…