Under positivity alone, the observational and interventional supports of an outcome variable coincide, enabling confounder-free causal inference on support-based estimands in N=1 settings.
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8 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 8representative citing papers
Develops an iDiD estimator for case-control sampling grounded in structural mean modeling that remains valid for IV candidates with time-invariant direct effects once selection bias is accounted for.
Large-scale study finds that counterfactual metrics on semi-simulated data do not select the same estimators as observable metrics on real data, and benchmark rankings fail to transfer.
The clone-censor-weight approach is formalized and tested via simulations before application to a breast cancer cohort comparing 2 versus 5 years of adjuvant tamoxifen, yielding estimates with substantial uncertainty.
PEQ-Net uses policy-aware reparameterization of ICE Q-functions and kernel mean embeddings in a shared encoder, followed by LTMLE, to jointly estimate multiple policies while constraining second-order bias for lower variance.
Provides necessary and sufficient conditions for ATE identifiability under selection bias by characterizing propensity and selection probabilities via weak assumptions on probability classes.
RepFlow combines representation learning and conditional flow matching to estimate both point and distributional causal effects while mitigating selection bias via entropically regularized Wasserstein distance on normalized latent representations.
Measurement error in latent confounders produces biased ATE estimates and miscalibrated intervals under conventional adjustment; a Bayesian joint model of measurement, treatment, and outcome is proposed to correct it.
citing papers explorer
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Causal Inference for Case Studies in Behavioral Health
Under positivity alone, the observational and interventional supports of an outcome variable coincide, enabling confounder-free causal inference on support-based estimands in N=1 settings.
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Instrumented difference-in-differences under case-control sampling
Develops an iDiD estimator for case-control sampling grounded in structural mean modeling that remains valid for IV candidates with time-invariant direct effects once selection bias is accounted for.
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Real vs. Semi-Simulated: Rethinking Evaluation for Treatment Effect Estimation
Large-scale study finds that counterfactual metrics on semi-simulated data do not select the same estimators as observable metrics on real data, and benchmark rankings fail to transfer.
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Estimating treatment duration effects via clone-censor-weight: a breast cancer case study
The clone-censor-weight approach is formalized and tested via simulations before application to a breast cancer cohort comparing 2 versus 5 years of adjuvant tamoxifen, yielding estimates with substantial uncertainty.
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Smooth Multi-Policy Causal Effect Estimation in Longitudinal Settings
PEQ-Net uses policy-aware reparameterization of ICE Q-functions and kernel mean embeddings in a shared encoder, followed by LTMLE, to jointly estimate multiple policies while constraining second-order bias for lower variance.
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Towards a holistic understanding of Selection Bias for Causal Effect Identification
Provides necessary and sufficient conditions for ATE identifiability under selection bias by characterizing propensity and selection probabilities via weak assumptions on probability classes.
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RepFlow: Representation Enhanced Flow Matching for Causal Effect Estimation
RepFlow combines representation learning and conditional flow matching to estimate both point and distributional causal effects while mitigating selection bias via entropically regularized Wasserstein distance on normalized latent representations.
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Measurement Induced Confounding
Measurement error in latent confounders produces biased ATE estimates and miscalibrated intervals under conventional adjustment; a Bayesian joint model of measurement, treatment, and outcome is proposed to correct it.