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.
Bart: Bayesian additive regression trees
7 Pith papers cite this work, alongside 1,515 external citations. Polarity classification is still indexing.
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UNVERDICTED 7representative citing papers
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.
Loss-weighted targeting in TMLE introduces more systematic bias than clever-covariate-scaled targeting under positivity stress, while a proposed Lepski-type adaptive truncation with brake improves stability over fixed rules like c/(sqrt(n) log n) with c=5 or 6.
A generalization of probabilistic reparameterization allows gradient-based acquisition optimization in fully mixed-variable Bayesian optimization with Gaussian process surrogates for non-equidistant discrete spaces.
A placebo-anchored cross-fitted doubly robust estimator for heterogeneous treatment effects in meta-analysis under covariate shift that improves accuracy at small target sample sizes.
New high-probability generalization bounds are derived via Rademacher complexity for regularized piecewise-linear regression trees, with an efficient GPU-based algorithm and empirical comparisons to piecewise-constant trees.
ShrinkageTrees is an R package implementing regularized Bayesian tree ensembles for survival outcomes and causal inference via AFT models, including the first Horseshoe Forest implementation.
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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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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.
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Investigating Targeting Strategies and Truncation in TMLE for the Average Treatment Effect under Practical Positivity Violations
Loss-weighted targeting in TMLE introduces more systematic bias than clever-covariate-scaled targeting under positivity stress, while a proposed Lepski-type adaptive truncation with brake improves stability over fixed rules like c/(sqrt(n) log n) with c=5 or 6.
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Bayesian Optimization for Mixed-Variable Problems in the Natural Sciences
A generalization of probabilistic reparameterization allows gradient-based acquisition optimization in fully mixed-variable Bayesian optimization with Gaussian process surrogates for non-equidistant discrete spaces.
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Transfer Learning for Meta-analysis Under Covariate Shift
A placebo-anchored cross-fitted doubly robust estimator for heterogeneous treatment effects in meta-analysis under covariate shift that improves accuracy at small target sample sizes.
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Efficient Regularized Piecewise-Linear Regression Trees
New high-probability generalization bounds are derived via Rademacher complexity for regularized piecewise-linear regression trees, with an efficient GPU-based algorithm and empirical comparisons to piecewise-constant trees.
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ShrinkageTrees: An R Package for Bayesian Tree Ensembles for Survival Analysis and Causal Inference
ShrinkageTrees is an R package implementing regularized Bayesian tree ensembles for survival outcomes and causal inference via AFT models, including the first Horseshoe Forest implementation.