10 papers
Learning to Fluctuate: Statistical Foundations for Causal Tabular Pretraining
Zhiheng Zhang
Causal tabular foundation models amortize effect estimation across synthetic mechanisms, but latent-effect supervision rewards posterior shrinkage rather than encoding the repeated…
Denoised Conformal Alignment for Reliable Selection of Conditional Average Treatment Effect Predictions
Xinyun Lu, Haoang Chi, Zhiheng Zhang
In selective deployment, practitioners act only on a model-chosen subset of individuals based on predicted conditional average treatment effects, but marginal conformal guarantees…
Wasserstein Policy Learning for Distributional Outcomes
Yiyan Huang, Cheuk Hang Leung, Qi Wu +1
Offline policy learning has received growing attention in causal inference. The primary objective is to learn a policy (individualized treatment rule) as a mapping from covariates…
Partial Identification under High-Dimensional Potential Outcomes and Confounders via Optimal Transport
Yunfeng Wang, Zhiheng Zhang, Zijun Gao
Partial identification provides informative causal guarantees when point identification is impossible, but existing approaches based on optimal transport (OT) become computationall…
Causal Representation Learning with Optimal Compression under Complex Treatments
Wanting Liang, Haoang Chi, Zhiheng Zhang
Estimating Individual Treatment Effects (ITE) in multi-treatment scenarios faces two critical challenges: the Hyperparameter Selection Dilemma for balancing weights and the Curse o…
Causal Matrix Completion under Multiple Treatments via Mixed Synthetic Nearest Neighbors
Minrui Luo, Zhiheng Zhang
Synthetic Nearest Neighbors (SNN) provides a principled solution to causal matrix completion under missing-not-at-random (MNAR) by exploiting local low-rank structure through fully…
Causal Influence Maximization with Steady-State Guarantees
Renjie Cao, Zhuoxin Yan, Zichen Wang +2
Classical influence maximization optimizes expected reach, which can be misaligned with welfare when exposures have heterogeneous, saturating, or adverse effects. We study seed sel…
Feasible Fusion: Constrained Joint Estimation under Structural Non-Overlap
Yuxi Du, Zhiheng Zhang, Haoxuan Li +4
Causal inference in modern largescale systems faces growing challenges, including highdimensional covariates, multi-valued treatments, massive observational (OBS) data, and limited…
Budgeted Active Experimentation for Treatment Effect Estimation from Observational and Randomized Data
Jiacan Gao, Xinyan Su, Mingyuan Ma +7
Estimating heterogeneous treatment effects is central to data-driven decision-making, yet industrial applications often face a fundamental tension between limited randomized contro…
Partial Identification with Proxy of Latent Confoundings via Sum-of-ratios Fractional Programming
Zhiheng Zhang
Due to the unobservability of confoundings, there has been widespread concern about how to compute causality quantitatively. To address this challenge, proxy-based negative control…