A two-stage causal estimator for semi-continuous exposures that disentangles exposure status and dose via a two-part propensity score in a marginal structural model.
The central role of the propensity score in observational studies for causal effects
4 Pith papers cite this work. Polarity classification is still indexing.
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DSL uses doubly robust pseudo-outcomes and a multi-output neural network to jointly estimate time-varying conditional average treatment effects for right-censored survival data.
Local Balance with Calibration using neural networks creates propensity score weights that enforce local covariate balance and calibration, yielding more stable weights and lower bias in average treatment effect estimates than prior nonparametric approaches.
EC is a Python library that formulates empirical calibration as convex optimization solved in dual form, with added support for multiple objectives, weight clipping, and inexact solutions.
citing papers explorer
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Two-stage Estimation for Causal Inference Involving a Semi-continuous Exposure
A two-stage causal estimator for semi-continuous exposures that disentangles exposure status and dose via a two-part propensity score in a marginal structural model.
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Estimating heterogeneous treatment effects with survival outcomes via a deep survival learner
DSL uses doubly robust pseudo-outcomes and a multi-output neural network to jointly estimate time-varying conditional average treatment effects for right-censored survival data.
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Local Balance Calibration for Nonparametric Propensity Score Estimation
Local Balance with Calibration using neural networks creates propensity score weights that enforce local covariate balance and calibration, yielding more stable weights and lower bias in average treatment effect estimates than prior nonparametric approaches.
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A Python Library For Empirical Calibration
EC is a Python library that formulates empirical calibration as convex optimization solved in dual form, with added support for multiple objectives, weight clipping, and inexact solutions.