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Found 16 papers, 3 papers with code
Statistical learning for constrained functional parameters in infinite-dimensional models with applications in fair machine learning
Constrained learning has become increasingly important, especially in the realm of algorithmic fairness and machine learning.
Targeted Machine Learning for Average Causal Effect Estimation Using the Front-Door Functional
As an alternative, the front-door criterion offers a solution, even in the presence of unmeasured confounders between treatment and outcome.
Sufficient Identification Conditions and Semiparametric Estimation under Missing Not at Random Mechanisms
This MNAR model corresponds to a so-called criss-cross structure considered in the literature on graphical models of missing data that prevents nonparametric identification of the entire missing data model.
Learning Task-Aware Effective Brain Connectivity for fMRI Analysis with Graph Neural Networks
To better adapt GNNs for fMRI analysis, we propose TBDS, an end-to-end framework based on \underline{T}ask-aware \underline{B}rain connectivity \underline{D}AG (short for Directed Acyclic Graph) \underline{S}tructure generation for fMRI analysis.
Causal and counterfactual views of missing data models
It is often said that the fundamental problem of causal inference is a missing data problem -- the comparison of responses to two hypothetical treatment assignments is made difficult because for every experimental unit only one potential response is observed.
On Testability of the Front-Door Model via Verma Constraints
The front-door criterion can be used to identify and compute causal effects despite the existence of unmeasured confounders between a treatment and outcome.
On Testability and Goodness of Fit Tests in Missing Data Models
Significant progress has been made in developing identification and estimation techniques for missing data problems where modeling assumptions can be described via a directed acyclic graph.
Causal Inference in the Presence of Interference in Sponsored Search Advertising
This assumption is violated in settings where units are related through a network of dependencies.
A Semiparametric Approach to Interpretable Machine Learning
In order to combat this shortcoming, we propose a novel approach to trading off interpretability and performance in prediction models using ideas from semiparametric statistics, allowing us to combine the interpretability of parametric regression models with performance of nonparametric methods.
Full Law Identification In Graphical Models Of Missing Data: Completeness Results
Missing data has the potential to affect analyses conducted in all fields of scientific study, including healthcare, economics, and the social sciences.
Semiparametric Inference For Causal Effects In Graphical Models With Hidden Variables
We derive influence function based estimators that exhibit double robustness for the identified effects in a large class of hidden variable DAGs where the treatment satisfies a simple graphical criterion; this class includes models yielding the adjustment and front-door functionals as special cases.
Optimal Training of Fair Predictive Models
Specifically, we show how to reparameterize the observed data likelihood such that fairness constraints correspond directly to parameters that appear in the likelihood, transforming a complex constrained optimization objective into a simple optimization problem with box constraints.
Identification In Missing Data Models Represented By Directed Acyclic Graphs
Missing data is a pervasive problem in data analyses, resulting in datasets that contain censored realizations of a target distribution.
Estimation of Personalized Effects Associated With Causal Pathways
For example, we may wish to maximize the chemical effect of a drug given data from an observational study where the chemical effect of the drug on the outcome is entangled with the indirect effect mediated by differential adherence.
Learning Optimal Fair Policies
Systematic discriminatory biases present in our society influence the way data is collected and stored, the way variables are defined, and the way scientific findings are put into practice as policy.
Fair Inference On Outcomes
We discuss a number of complications that arise in classical statistical inference due to this view and provide workarounds based on recent work in causal and semi-parametric inference.
