Advanced Causal Inference Techniques

Latest papers

• Incentive Alignment in Online Experimentation

  {Ermis Soumalias, Richard Mudd, Abbas Zaidi · 6 October 2026

  Evaluating the causal effect of new features is a central goal for online platforms. While recent literature addresses limited testing traffic via centralized portfolio optimization, this perspective abstracts away a critical institutional reality: experimentation is operationally decentralized. The…

• Peer Effects in Signed Networks: Separating Influence Through Positive and Negative Ties

  Xiaojing Du, Jiuyong Li, Lin Liu, Debo Cheng, Jixue Liu, Thuc Duy Le · 5 October 2026

  Evaluating network interventions requires understanding how treatment affects people through their social relationships. Counting treated neighbors without distinguishing supportive and antagonistic ties can conceal opposing influences. We define effects through positive and negative ties, their int…

• Target-Dependent Limits of Causal Repair: A Leading-Log Frontier in a Gaussian Model

  Qinchuan Cheng, Jiaqi Liu, Ruixuan Xie · 2 October 2026

  Knowing how much a causal predictor could improve need not reveal the gain of the repair actually learned. We quantify this gap in a scalar Gaussian causal experiment with known intervention geometry: auxiliary data identify effect magnitude up to bounded contamination, while diagnostics identify di…

• Sample What You Say: Aligning Language Models to Sample the Distributions They State

  Kasra Arabi, Virginia Smith, Chhavi Yadav · 30 September 2026

  Language models are increasingly used to sample from a specified distribution, for instance, to simulate survey respondents or generate synthetic data. Instruction-tuned models can state such a distribution correctly and still fail to sample from it. Prompting and changes to decoding reduce this mis…

• AI-based matching improves refugee employment in a double-blind randomized trial

  Kirk Bansak, Jens Hainmueller, Dominik Hangartner, Jeremy Ferwerda, Elisabeth Paulson, Angie Delevoye, Nicholas Adams-Cohen, Ashwin Ramaswami, Selina Kurer, Joelle Pianzola, Michael Hotard · 29 September 2026

  Refugee integration is a central policy challenge for host countries, and where governments initially place refugees shapes their integration trajectories. Yet placement officers often have limited information about where each case is most likely to succeed. Algorithmic refugee matching uses adminis…

• Measurement-Error-Aware Causal Distributed-Lag Quantile Modeling of Indoor Air Pollution and Short-Term Lung-Function Deterioration

  Shayma Alkobaisi, Anas Ali · 29 September 2026

  Low-cost indoor air-quality sensors could support personalized asthma prevention, but their nonlinear measurement error, delayed exposure effects, time-varying confounding, and heterogeneous lower-tail responses limit risk estimation. We present CAUSALQUANT-ASTHMA, a measurement-error-aware causal q…

• Peer-Grounded Counterfactual Path Planning for Chronic Health Management

  Saman Khamesian, Hassan Ghasemzadeh · 28 September 2026

  Effective behavioral intervention in chronic disease management requires not a single prescription but a sequence of incremental steps, each grounded in what real, similar individuals have demonstrably achieved. Counterfactual explanation offers a natural computational route to such guidance, answer…

• Counterfactual Online Conformal Prediction Under Adaptive Logging

  Xinyu Qiao, Yichen Lin, Kaihong Ji, Xue Wang, Tao Yao · 28 September 2026

  Online conformal prediction can fail when predictions shape actions and actions determine which outcomes enter calibration. Standard adaptive methods may retain marginal coverage while systematically miscovering the counterfactual outcomes of rarely selected actions. This paper formalizes the failur…

• Offline Policy Evaluation as a decision support tool for designing Adaptive Experiments

  Jo\~ao Victor Ferreira Alves, Eduardo Rocha Laurentino, Gustavo de Oliveira Kanno, Thiago Costa Rizuti da Rocha · 28 September 2026

  We investigate how historical data from fixed randomized experiments (A/B tests) can be used to inform the deployment of adaptive experiments based on contextual bandits. Given data collected under a static allocation, our goal is to assess which adaptive policies, if any, would have outperformed th…

• Which Influence Are We Estimating? The Role of Counterfactual Specifications in Data Attribution

  Zhe Li, Wei Zhao, Peixin Zhang, Jun Sun · 28 September 2026

  Estimating the influence of training examples on model behavior is essential for data debugging, valuation, and attribution. Existing influence estimators often produce incompatible rankings, which are commonly ascribed to approximation error. We argue that a more fundamental source of disagreement …

• GeoDose-CP: Graph-Local Conformal Inference for Continuous-Treatment Earth Observation

  Md Khalid Hasan Sakib, Dristi Datta, Manoranjan Paul, Davina White · 25 September 2026

  Reliable intervention-oriented uncertainty quantification from Earth observation (EO) remains challenging when continuous treatment shifts, spatial dependence, limited support, and satellite-outcome uncertainty must be addressed simultaneously. Existing causal, conformal, and spatial approaches addr…

• Diverse Geometries, Frozen Weights: Robust Heterogeneous Treatment-Effect Estimation via Causal Expert Ensembles

  Ali Haghpanah Jahromi, Mohammad Taheri · 25 September 2026

  Estimating heterogeneous treatment effects from observational data is difficult because the most appropriate inductive bias varies with overlap, treatment imbalance, prognostic structure, and sample size. We introduce the Geometry-Diverse Anchor-Correction Expert Ensemble (GeoACE), a five-expert fra…

• Beyond the Illusion of Power: Calibrating Quasi-Experiments in Observational IS

  Spandan Ghose Chowdhury · 24 September 2026

  Information systems (IS) researchers increasingly use quasi-experimental methods such as difference-in-differences (DiD) and instrumental variables (IV) to recover causal effects from observational panel data. Power calculations that justify these designs assume i.i.d. errors, but the deeper problem…

• Artificial intelligence surrogates for treatment effect estimation with before-and-after data

  Frances Dean, Anna Neufeld, Joshua Barrios, Geoffrey H Tison, Ahmed Alaa · 24 September 2026

  Estimating the causal effects of medical treatments is difficult when clinically important outcomes are costly to measure or require long follow-up. Short-term or inexpensive surrogate outcomes offer a potential alternative, but surrogate biomarkers may be unavailable or difficult to identify. Advan…

• Learning Risk Scores Robust to Unobserved Confounders

  Ryan Edmonds, Yingxiao Ye, Sina Aghaei, Andr\'es G\'omez, \c{C}a\u{g}{\i}l Ko\c{c}yi\u{g}it, Phebe Vayanos · 24 September 2026

  We consider the problem of learning risk scores to prioritize individuals for scarce resources or interventions, from historical observational data affected by unobserved confounding. Decisions about who receives scarce resources are often guided by risk scores based on recorded characteristics, suc…

• KITE: Scaling Jev Population Experiments with Sparse Flagship Calibration

  Hengyu Li (The University of Tokyo) · 24 September 2026

  KITE queries a typed behavioral kernel once per unique state, then executes populations of any size from the table with event-keyed randomness and common random numbers. An expensive flagship model is reserved for sparse paired anchors that estimate intervention effects. Measured human-model discrep…

• Conditional Distributional Treatment Effects: Doubly Robust Estimation and Testing

  Saksham Jain, Alex Luedtke · 23 September 2026

  Beyond conditional average treatment effects, treatments may impact the entire outcome distribution in covariate-dependent ways, for example, by altering the variance or tail risks for specific subpopulations. We propose a novel estimand to capture such conditional distributional treatment effects, …

• Learning to Fluctuate: Statistical Foundations for Causal Tabular Pretraining

  Zhiheng Zhang · 23 September 2026

  Causal tabular foundation models amortize effect estimation across synthetic mechanisms, but latent-effect supervision rewards posterior shrinkage instead of directly encoding the repeated-sample response needed in a fixed deployment population. We introduce fluctuation-supervised pretraining (FSP):…

• Conditional Tensor Diffusion: Distributional Counterfactual Learning and Inference

  Xinbing Kong, Zeyu Li, Junfan Mao, Bin Wu · 23 September 2026

  Causal inference guides operational and managerial decisions but remains challenging in high-dimensional panel or tensor settings, where decisions may depend on the joint conditional distribution of missing control outcomes. We develop \emph{Counterfactual Tucker Diffusion} (\CFTDiff), which integra…

• Optimal Multi-way Decision Trees for Stratified Sampling in Online Controlled Experiments

  Tomoka Takei, Shunnosuke Ikeda, Yuichi Takano · 22 September 2026

  Online controlled experiments, or A/B tests, are widely used to estimate causal effects on digital platforms. A central challenge is to improve experimental sensitivity, or statistical power, without increasing the experimental sample size. Stratified sampling is a classical variance reduction techn…

• Causal Inference with Unobserved Confounding: A Mixture Learning Perspective

  Mansi Sood, Devavrat Shah · 22 September 2026

  Unobserved confounding is a fundamental challenge in causal inference from observational data. This article develops a mixture-learning perspective, viewing latent confounders as sources of heterogeneity that induce mixture structure in observed data. Under suitable structural and identifiability as…

• Counterfactual Tool Ranking under Utility, Cost, and Privilege Constraints

  Jiapeng Li · 22 September 2026

  Counterfactual tool evaluation must distinguish authority, historical support, and what a comparison actually estimates. We study these distinctions with eleven executable enterprise-inspired tools, exact-propensity logs, and real local Model Context Protocol transport. An initial 45-run synthetic s…

• Stable Policy Learning

  Harvey Barnhard, Giacomo Opocher, Rahul Singh · 18 September 2026

  In evidence-based policymaking, typically one experimental sample is observed, then a learned policy recommendation is implemented at scale. Policies learned from the experimental data can perform well in expected welfare, yet random sampling in the experiment can produce recommendations with poor w…

• Risk-Set Transported Synthetic Control with Difference-in-Differences Adjustment under Staggered Treatment Adoption

  Mojtaba Eslami · 18 September 2026

  In staggered treatment-adoption designs, later-treated units are valid controls for an earlier-treated cohort only until their own treatment begins, so the admissible donor set contracts with event time. Fixing the donor pool at the longest horizon discards temporarily eligible donors, whereas re-es…

• Information Set Emulation: Causal Certificates for AI Derived EHR Features

  Takes Fujita (VRI), Nobutaka Hattori (Department of Neurology, Juntendo University School of Medicine) · 17 September 2026

  AI and large language models can recover clinically meaningful features from electronic health records (EHRs), but predictive usefulness does not establish admissibility for causal inference. We introduce information set emulation: an AI typed lift attaches source evidence, clinical and recording ti…