Advanced Causal Inference Techniques

Derniers papiers

• Conditional Distributional Treatment Effects: Doubly Robust Estimation and Testing

  Saksham Jain, Alex Luedtke · 23 septembre 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 septembre 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 septembre 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 septembre 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 septembre 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 septembre 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 septembre 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 septembre 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 septembre 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…

• Conformal Policy Learning with Distribution-Free Safety Guarantees

  Ying Jin, Naoki Egami · 16 septembre 2026

  Policy learning aims to determine who should be treated based on individual characteristics. In high-stakes settings such as medicine and public policy where safety is a central concern, improving the average outcomes alone may not be sufficient: decision makers may also seek to protect individuals …

• Splitting the Difference: Interpretable Causal Forests for Treatment Effect Heterogeneity and Bias

  Nicolas Alexander Ihlo, Merle Behr · 16 septembre 2026

  In various fields, such as medicine and marketing, accurately predicting individual treatment effects holds significant promise. However, achieving reliable predictions alone is often insufficient for making informed decisions; it is equally important to understand why the treatment effect is higher…

• Synthetic Blips: Generalizing Synthetic Controls for Dynamic Treatment Effects

  Anish Agarwal, Sukjin Han, Dwaipayan Saha, Vasilis Syrgkanis, Haeyeon Yoon · 14 septembre 2026

  We propose a generalization of the synthetic control methods to the setting with dynamic treatment effects, in which each unit receives multiple treatments sequentially, according to an adaptive policy that depends on a latent, endogenously time-varying confounding state. Under a low-rank latent fac…

• Counterfactual Marginalisation: Framework for Evaluating Robustness to Nuisance Variables

  Yasin Ibrahim, Hermione Warr, Robin J. Evans, Konstantinos Kamnitsas · 11 septembre 2026

  Machine learning models can achieve strong test performance while relying on demographic or acquisition-related shortcuts. We propose counterfactual (CF) marginalisation as a test-time evaluation procedure for assessing robustness of classification models to such variables. Given a CF image generato…

• Differentially Private Average Treatment Effect Estimation by Propensity Score Blocking

  Duncan Stewardson, Grayson W. White, Adam Groce · 10 septembre 2026

  Average treatment effect (ATE) estimation in observational studies is a fundamental statistical tool used frequently in social science, medicine, and other fields. These fields often work with sensitive data where privacy protections are important, so a differentially private mechanism for ATE estim…

• Confounding-Valid Conformal Inference for Counterfactual KPIs in Wireless Networks

  Abdessamed Qchohi, Jessica Moysen Cortes, Matteo Zecchin · 7 septembre 2026

  Conformal counterfactual inference enables network operators to use logged telemetry to reliably answer 'what-if' questions about network operation. These answers typically take the form of prediction sets that contain, with a user-defined probability, the key performance indicators (KPIs) that woul…

• A location-invariant estimator of extremal quantile treatment effects for heavy-tailed distributions

  Xin Yu, Shuwei Huang, Jicheng Liu, Jielin Tang, Bolin Wang, Yunxiao Zhang, Tian Zhao · 4 septembre 2026

  Quantile treatment effects (QTEs) measure the effect of a treatment on the distribution of an outcome, and their estimation at extreme quantile levels is of central interest in applications where the target quantiles lie far beyond the range of the data. For heavy-tailed potential outcomes, existing…

• When Prediction Error Is Not Enough: Evaluating Nuisance-Function Prediction for Causal Estimation

  Cong Cao · 2 septembre 2026

  Prediction error is widely used to evaluate nuisance-function estimators in causal inference, but its relationship with causal estimator performance may differ across performance measures. We studied this question in a partially linear model using Monte Carlo simulations. We compared ordinary least …

• Difference-in-Differences on a Censored Rating Scale Can Manufacture an Effect: Evidence from a Pre-Registered LLM-Judge Audit

  Shuyi Fan, Boyuan Deng, Mengyu Xu, Xinhong Xie, Chenyang Li, Hongyang Zhang · 28 août 2026

  Audits of LLM judges certify a bias by contrasting matched conditions, and the strongest designs difference twice: a within-item contrast between two candidate responses, differenced again across a manipulated attribute, read off a bounded rating scale. We show that this endpoint is not identified o…

• DIRECT: Decomposing Audience Preference and Creative Effect in Visual Content Analytics

  Yizhi Liu, Balaji Padmanabhan, Siva Viswanathan · 28 août 2026

  Which visual choices make a post perform better? A growing literature answers this question with pooled coefficients estimated across many creators, which platforms translate into creative recommendations. We show that these coefficients blend two distinct patterns that can point in opposite directi…

• Generative AI for Validating Physics Laws

  Maria Nareklishvili, Nicholas Polson, Vadim Sokolov · 26 août 2026

  We propose generative learner for estimating heterogeneous treatment effects and characterizing the full distribution of causal effects. The learner takes the form of a multi-head feed-forward neural network with three jointly estimated subnetworks, propensity score, baseline outcome, and heterogene…

• Barycentric Fused Gromov-Wasserstein Balancing for Causal Inference under Multiple Treatments

  Yuki Murakami, Takumi Hattori, Kohsuke Kubota · 25 août 2026

  Estimating heterogeneous single and interaction treatment effects from observational data under multiple simultaneous treatments is crucial for decision-making. To mitigate estimation variance, previous studies balance representation distributions between every pair of treatment patterns. However, s…

• Causal Inference under Interference with Learned Exposure Mappings

  Cong Cao · 21 août 2026

  Exposure mappings are often assumed to be known in causal spillover analyses. In environmental settings, however, they are typically induced by transport processes that are not directly observed and must instead be learned from pollution data. We study how uncertainty in learned transport processes …

• Transportable Causal Effect Estimation across Networks under Interference

  Xiaojing Du, Jiuyong Li, Lin Liu, Debo Cheng, Jixue Liu, Thuc Duy Le · 20 août 2026

  Estimating causal effects under network interference typically assumes that the network used for training and the network used for deployment coincide. In practice, an intervention is run on one population while the question of interest concerns a different population, and the two generally differ i…

• Cross-View Correspondence Is a Measurement Intervention: Two-Sided Validation for Agent Evaluation and Credit Assignment

  Zhen Zhang, Ahmad Hafez, Amr Alanwar · 19 août 2026

  Agent evaluations and trace-based learning often compare outputs across transformed views through a post-response correspondence treated as neutral preprocessing. We show that this correspondence is a measurement intervention: omitting it can manufacture sensitivity, an over-aggressive map can manuf…

• Second-Order Policy Effects as State Transitions: A Source-Linked Benchmark for Policy Simulation

  Wesley Shu · 18 août 2026

  Policy evaluation often estimates direct benefits and costs while treating the institutional environment as fixed. In practice, a policy changes the system it enters: actors adapt, enforcement capacity shifts, burdens move, and new equilibria form around capture, gaming, compliance theater, irrevers…