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Found 12 papers, 3 papers with code
Efficient and Multiply Robust Risk Estimation under General Forms of Dataset Shift
Despite extensive literature on dataset shift, limited works address how to efficiently use the auxiliary populations to improve the accuracy of risk evaluation for a given machine learning task in the target population.
Partial Identification of Causal Effects Using Proxy Variables
However, completeness is well-known not to be empirically testable, and although a bridge function may be well-defined, lack of completeness, sometimes manifested by availability of a single type of proxy, may severely limit prospects for identification of a bridge function and thus a causal effect; therefore, potentially restricting the application of the proximal causal framework.
Prediction Sets Adaptive to Unknown Covariate Shift
Predicting sets of outcomes -- instead of unique outcomes -- is a promising solution to uncertainty quantification in statistical learning.
Validating Causal Inference Methods
Thus simulated data sets are used to evaluate the potential performance of various causal estimation methods when applied to data similar to the observed sample.
Combining Experimental and Observational Data for Identification and Estimation of Long-Term Causal Effects
We consider the task of identifying and estimating the causal effect of a treatment variable on a long-term outcome variable using data from an observational domain and an experimental domain.
A General Framework for Treatment Effect Estimation in Semi-Supervised and High Dimensional Settings
Specifically, we consider two such estimands: (a) the average treatment effect and (b) the quantile treatment effect, as prototype cases, in an SS setting, characterized by two available data sets: (i) a labeled data set of size $n$, providing observations for a response and a set of high dimensional covariates, as well as a binary treatment indicator; and (ii) an unlabeled data set of size $N$, much larger than $n$, but without the response observed.
Causal Inference with Hidden Mediators
In this paper, we extend the proximal causal inference approach to settings where identification of causal effects hinges upon a set of mediators which are not observed, yet error prone proxies of the hidden mediators are measured.
End-to-End Balancing for Causal Continuous Treatment-Effect Estimation
In the process of optimization, these weights are automatically tuned to the specific dataset and causal inference algorithm being used.
Minimax Kernel Machine Learning for a Class of Doubly Robust Functionals with Application to Proximal Causal Inference
In this paper, we first extend the class of Robins et al. to include doubly robust IFs in which the nuisance functions are solutions to integral equations.
On a necessary and sufficient identification condition of optimal treatment regimes with an instrumental variable
Unmeasured confounding is a threat to causal inference and individualized decision making.
Statistics Theory Methodology Statistics Theory
A semiparametric instrumental variable approach to optimal treatment regimes under endogeneity
There is a fast-growing literature on estimating optimal treatment regimes based on randomized trials or observational studies under a key identifying condition of no unmeasured confounding.
Selective machine learning of doubly robust functionals
While model selection is a well-studied topic in parametric and nonparametric regression or density estimation, selection of possibly high-dimensional nuisance parameters in semiparametric problems is far less developed.
