Search Results for author:
Found 12 papers, 9 papers with code
Causal Machine Learning for Cost-Effective Allocation of Development Aid
Then, we demonstrate our framework using real-world HIV data.
Causal Fairness under Unobserved Confounding: A Neural Sensitivity Framework
This enables practitioners to examine the sensitivity of their machine learning models to unobserved confounding in fairness-critical applications.
A Neural Framework for Generalized Causal Sensitivity Analysis
Unobserved confounding is common in many applications, making causal inference from observational data challenging.
Bounds on Representation-Induced Confounding Bias for Treatment Effect Estimation
In this paper, we propose a new, representation-agnostic refutation framework for estimating bounds on the representation-induced confounding bias that comes from dimensionality reduction (or other constraints on the representations) in CATE estimation.
Counterfactual Fairness for Predictions using Generative Adversarial Networks
It is often achieved through counterfactual fairness, which ensures that the prediction for an individual is the same as that in a counterfactual world under a different sensitive attribute.
Bayesian Neural Controlled Differential Equations for Treatment Effect Estimation
Treatment effect estimation in continuous time is crucial for personalized medicine.
Partial Counterfactual Identification of Continuous Outcomes with a Curvature Sensitivity Model
We further show that existing point counterfactual identification methods are special cases of our Curvature Sensitivity Model when the bound of the curvature is set to zero.
Fair Off-Policy Learning from Observational Data
Algorithmic decision-making in practice must be fair for legal, ethical, and societal reasons.
Normalizing Flows for Interventional Density Estimation
To the best of our knowledge, our Interventional Normalizing Flows are the first proper fully-parametric, deep learning method for density estimation of potential outcomes.
Estimating individual treatment effects under unobserved confounding using binary instruments
(2)~We further show that our framework asymptotically outperforms state-of-the-art plug-in IV methods for CATE estimation, in the sense that it achieves a faster rate of convergence if the CATE is smoother than the individual outcome surfaces.
Causal Transformer for Estimating Counterfactual Outcomes
In this paper, we develop a novel Causal Transformer for estimating counterfactual outcomes over time.
Estimating average causal effects from patient trajectories
In medical practice, treatments are selected based on the expected causal effects on patient outcomes.
