Presents the first kernel framework for distributional treatment effect inference from adaptively collected data, using doubly robust RKHS scores, cross-fold witness functions, and sequentially normalized statistics with valid type-I error.
Bayesian nonparametric modeling for causal inference.Journal of Computational and Graphical Statistics, 20:217–240, 03 2011
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Parametric models for principal causal effects produce only partial identification without principal ignorability, with association parameters for strata identifiable solely under violation of that assumption plus strong parametric constraints.
CausalGuard aggregates LLM-proposed and data-pruned DAGs to weight doubly robust pseudo-outcomes and applies conformal calibration to deliver finite-sample marginal coverage for conditional average treatment effects under graph uncertainty.
A neural doubly robust proxy causal learning framework using mean embeddings for treatment bridges provides consistent estimators for causal dose-response functions under unobserved confounding for continuous and structured treatments.
CIVeX maps agent tool calls to structural causal queries, checks identifiability, and issues auditable verdicts to prevent false executions while preserving utility on confounded benchmarks.
A framework using generative AI to produce synthetic multilevel data for Monte Carlo simulations that evaluate the performance and parameter recovery of quantitative methods.
RG-inspired lattice models for piecewise GLMs provide explicit interpretable partitions and a replica-analysis-derived scaling law for regularization that allows increasing complexity without expected rise in generalization loss.
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Kernel Treatment Effects with Adaptively Collected Data
Presents the first kernel framework for distributional treatment effect inference from adaptively collected data, using doubly robust RKHS scores, cross-fold witness functions, and sequentially normalized statistics with valid type-I error.
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Partial identification of principal causal effects under violations of principal ignorability
Parametric models for principal causal effects produce only partial identification without principal ignorability, with association parameters for strata identifiable solely under violation of that assumption plus strong parametric constraints.
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CausalGuard: Conformal Inference under Graph Uncertainty
CausalGuard aggregates LLM-proposed and data-pruned DAGs to weight doubly robust pseudo-outcomes and applies conformal calibration to deliver finite-sample marginal coverage for conditional average treatment effects under graph uncertainty.
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Doubly Robust Proxy Causal Learning with Neural Mean Embeddings
A neural doubly robust proxy causal learning framework using mean embeddings for treatment bridges provides consistent estimators for causal dose-response functions under unobserved confounding for continuous and structured treatments.
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CIVeX: Causal Intervention Verification for Language Agents
CIVeX maps agent tool calls to structural causal queries, checks identifiability, and issues auditable verdicts to prevent false executions while preserving utility on confounded benchmarks.
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Generative AI-Based Monte Carlo Simulation for Method Evaluation Using Synthetic Multilevel Data
A framework using generative AI to produce synthetic multilevel data for Monte Carlo simulations that evaluate the performance and parameter recovery of quantitative methods.
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A renormalization-group inspired lattice-based framework for piecewise generalized linear models
RG-inspired lattice models for piecewise GLMs provide explicit interpretable partitions and a replica-analysis-derived scaling law for regularization that allows increasing complexity without expected rise in generalization loss.