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Found 9 papers, 5 papers with code
Nonparametric estimation of a covariate-adjusted counterfactual treatment regimen response curve
Third, we provide consistency and convergence rate for the optimizer of the regimen-response curve estimator; this enables us to estimate an optimal semiparametric rule.
Causal inference for the expected number of recurrent events in the presence of a terminal event
We study causal inference and efficient estimation for the expected number of recurrent events in the presence of a terminal event.
Inference for relative sparsity
Further, in the relative sparsity work, the authors only considered the single-stage decision case; here, we consider the more general, multi-stage case.
Relative Sparsity for Medical Decision Problems
This end is facilitated if one can pinpoint the aspects of the policy (i. e., the parameters for blood pressure and heart rate) that change when moving from the standard of care to the new, suggested policy.
Instrumented Difference-in-Differences
Unmeasured confounding is a key threat to reliable causal inference based on observational studies.
Causal Inference
Methodology
Nonparametric inverse probability weighted estimators based on the highly adaptive lasso
We propose a class of nonparametric inverse probability weighted estimators in which the weighting mechanism is estimated via undersmoothing of the highly adaptive lasso, a nonparametric regression function proven to converge at $n^{-1/3}$-rate to the true weighting mechanism.
Robust Q-learning
Q-learning is a regression-based approach that is widely used to formalize the development of an optimal dynamic treatment strategy.
Outcome-adaptive lasso: variable selection for causal inference
Traditionally, a “throw in the kitchen sink” approach has been used to select covariates for inclusion into the propensity score, but recent work shows including unnecessary covariates can impact both the bias and statistical efficiency of propensity score estimators.
Constructing Dynamic Treatment Regimes in Infinite-Horizon Settings
The application of existing methods for constructing optimal dynamic treatment regimes is limited to cases where investigators are interested in optimizing a utility function over a fixed period of time (finite horizon).
