DR-ME is the first semiparametrically efficient finite-location kernel test for interpretable distributional treatment effects, using orthogonal doubly robust features derived from observational data.
Yuta Saito, Shunsuke Aihara, Megumi Matsutani, and Yusuke Narita
7 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 7verdicts
UNVERDICTED 7representative citing papers
A GEE-based stacked M-estimation framework merges propensity score and marginal structural models to directly compute the large-sample variance of the IPTW estimator from pilot data for prospective sample size planning, with bootstrap stabilization.
CDSP uses an effect-size asymmetry assumption and statistical power to estimate causal directions from bivariate data with uncertainty, reducing false discoveries by 18% on 100 benchmark pairs.
75% of massive compact quiescent galaxies at z~0 require three-component photometric models (bulge + disk + envelope), versus only 7% of mass-matched control quiescent galaxies.
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.
Stochastic causal representation learning with sMMD resolves the bias-precision paradox, improving accuracy under distribution shift by up to 11.5% and clinician accuracy by 14.7% on large ICU cohorts.
A recursive Riesz representer-based targeted minimum loss estimation procedure unifies asymptotically efficient estimation of causal estimands such as time-varying treatment effects and mediation effects.
citing papers explorer
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Semiparametric Efficient Test for Interpretable Distributional Treatment Effects
DR-ME is the first semiparametrically efficient finite-location kernel test for interpretable distributional treatment effects, using orthogonal doubly robust features derived from observational data.
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Estimator-Aligned Prospective Sample Size Determination for Designs Using Inverse Probability of Treatment Weighting
A GEE-based stacked M-estimation framework merges propensity score and marginal structural models to directly compute the large-sample variance of the IPTW estimator from pilot data for prospective sample size planning, with bootstrap stabilization.
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Causal Discovery via Statistical Power (CDSP)
CDSP uses an effect-size asymmetry assumption and statistical power to estimate causal directions from bivariate data with uncertainty, reducing false discoveries by 18% on 100 benchmark pairs.
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Small and Complex I: The Three Component Structure of $z \sim 0$ Massive Compact Quiescent Galaxies
75% of massive compact quiescent galaxies at z~0 require three-component photometric models (bulge + disk + envelope), versus only 7% of mass-matched control quiescent galaxies.
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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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Resolving the bias-precision paradox with stochastic causal representation learning for personalized medicine
Stochastic causal representation learning with sMMD resolves the bias-precision paradox, improving accuracy under distribution shift by up to 11.5% and clinician accuracy by 14.7% on large ICU cohorts.
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A Riesz Representer Perspective on Targeted Learning
A recursive Riesz representer-based targeted minimum loss estimation procedure unifies asymptotically efficient estimation of causal estimands such as time-varying treatment effects and mediation effects.