Develops single-world marginal separable effects as full-population causal estimands for outcomes truncated by death, provides identification and estimation results, and demonstrates them via reanalysis of a prostate cancer trial.
arXiv preprint arXiv:2009.10982 , year=
9 Pith papers cite this work. Polarity classification is still indexing.
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The paper defines computational identifiability as success of a finite search procedure in finding an empirical estimator for a causal query within error tolerance, conditional on the search assumptions and procedure.
MediEncoder jointly learns nonlinear low-dimensional covariate and mediator representations via a coupled encoder-decoder with cross-factor network, then applies them in an efficient influence function estimator for natural direct and indirect effects.
Proximal confounding bridge functions yield four nonparametric identification strategies and a quadruply robust estimator for path-specific effects under unmeasured confounding.
A meta-learning method identifies the conditional mean of task-specific causal demand parameters by conditioning on all prices while masking two demand outcomes, assuming at least two locally exogenous prices per task.
Interventions in LLM-simulated user experiments induce distribution shifts in latent attributes that create confounding bias, diagnosable with negative control outcomes and partially mitigated by adding setting-relevant persona details.
Causal effects are identifiable for categorical unobserved confounders via mixture learning and tensor decomposition, yielding consistent estimators with non-asymptotic guarantees.
A new adaptive variance estimator for relative sparsity coefficients is introduced that fully utilizes the prior asymptotic normality theorem and incorporates variable selection effects.
A diagnostic that measures instability of constraint-based causal graphs over increasing conditioning depths to detect hidden confounding or incomplete state in time series observational data.
citing papers explorer
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Causal Inference for All: Marginal Estimands for Outcomes Truncated by Death
Develops single-world marginal separable effects as full-population causal estimands for outcomes truncated by death, provides identification and estimation results, and demonstrates them via reanalysis of a prostate cancer trial.
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Computational Identifiability
The paper defines computational identifiability as success of a finite search procedure in finding an empirical estimator for a causal query within error tolerance, conditional on the search assumptions and procedure.
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MediEncoder: Nonlinear Representation Learning for High-Dimensional Causal Mediation Analysis
MediEncoder jointly learns nonlinear low-dimensional covariate and mediator representations via a coupled encoder-decoder with cross-factor network, then applies them in an efficient influence function estimator for natural direct and indirect effects.
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Proximal Path-Specific Inference
Proximal confounding bridge functions yield four nonparametric identification strategies and a quadruply robust estimator for path-specific effects under unmeasured confounding.
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Causal Multi-Task Demand Learning
A meta-learning method identifies the conditional mean of task-specific causal demand parameters by conditioning on all prices while masking two demand outcomes, assuming at least two locally exogenous prices per task.
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The Illusion of Intervention: Your LLM-Simulated Experiment is an Observational Study
Interventions in LLM-simulated user experiments induce distribution shifts in latent attributes that create confounding bias, diagnosable with negative control outcomes and partially mitigated by adding setting-relevant persona details.
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Causal Inference with Categorical Unobserved Confounder via Mixture Learning
Causal effects are identifiable for categorical unobserved confounders via mixture learning and tensor decomposition, yielding consistent estimators with non-asymptotic guarantees.
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An adaptive variance estimator for relative sparsity
A new adaptive variance estimator for relative sparsity coefficients is introduced that fully utilizes the prior asymptotic normality theorem and incorporates variable selection effects.
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Markovianity-Based Conditioning Depth Diagnostics for Hidden Confounding in Observational Datasets
A diagnostic that measures instability of constraint-based causal graphs over increasing conditioning depths to detect hidden confounding or incomplete state in time series observational data.