Related papers: Double Robust Representation Learning for Counterfactual Prediction

Double Robust Representation Learning for Counterfactual Prediction

URL: http://arxiv.org/abs/2010.07866v2
Date: Fri, 16 Oct 2020 21:32:28 GMT
Title: Double Robust Representation Learning for Counterfactual Prediction
Authors: Shuxi Zeng, Serge Assaad, Chenyang Tao, Shounak Datta, Lawrence Carin, Fan Li
Abstract summary: We propose a novel scalable method to learn double-robust representations for counterfactual predictions. We make robust and efficient counterfactual predictions for both individual and average treatment effects. The algorithm shows competitive performance with the state-of-the-art on real world and synthetic data.
Score: 68.78210173955001
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Causal inference, or counterfactual prediction, is central to decision making in healthcare, policy and social sciences. To de-bias causal estimators with high-dimensional data in observational studies, recent advances suggest the importance of combining machine learning models for both the propensity score and the outcome function. We propose a novel scalable method to learn double-robust representations for counterfactual predictions, leading to consistent causal estimation if the model for either the propensity score or the outcome, but not necessarily both, is correctly specified. Specifically, we use the entropy balancing method to learn the weights that minimize the Jensen-Shannon divergence of the representation between the treated and control groups, based on which we make robust and efficient counterfactual predictions for both individual and average treatment effects. We provide theoretical justifications for the proposed method. The algorithm shows competitive performance with the state-of-the-art on real world and synthetic data.

Related papers

Zero-Shot Uncertainty Quantification using Diffusion Probabilistic Models [7.136205674624813]
We conduct a study to evaluate the effectiveness of ensemble methods on solving different regression problems using diffusion models. We demonstrate that ensemble methods consistently improve model prediction accuracy across various regression tasks. Our study provides a comprehensive view of the utility of diffusion ensembles, serving as a useful reference for practitioners employing diffusion models in regression problem-solving.
arXiv Detail & Related papers (2024-08-08T18:34:52Z)
CogDPM: Diffusion Probabilistic Models via Cognitive Predictive Coding [62.075029712357]
This work introduces the Cognitive Diffusion Probabilistic Models (CogDPM) CogDPM features a precision estimation method based on the hierarchical sampling capabilities of diffusion models and weight the guidance with precision weights estimated by the inherent property of diffusion models. We apply CogDPM to real-world prediction tasks using the United Kindom precipitation and surface wind datasets.
arXiv Detail & Related papers (2024-05-03T15:54:50Z)
Prediction-Powered Inference [68.97619568620709]
Prediction-powered inference is a framework for performing valid statistical inference when an experimental dataset is supplemented with predictions from a machine-learning system. The framework yields simple algorithms for computing provably valid confidence intervals for quantities such as means, quantiles, and linear and logistic regression coefficients. Prediction-powered inference could enable researchers to draw valid and more data-efficient conclusions using machine learning.
arXiv Detail & Related papers (2023-01-23T18:59:28Z)
Benign-Overfitting in Conditional Average Treatment Effect Prediction with Linear Regression [14.493176427999028]
We study the benign overfitting theory in the prediction of the conditional average treatment effect (CATE) with linear regression models. We show that the T-learner fails to achieve the consistency except the random assignment, while the IPW-learner converges the risk to zero if the propensity score is known.
arXiv Detail & Related papers (2022-02-10T18:51:52Z)
Test-time Collective Prediction [73.74982509510961]
Multiple parties in machine learning want to jointly make predictions on future test points. Agents wish to benefit from the collective expertise of the full set of agents, but may not be willing to release their data or model parameters. We explore a decentralized mechanism to make collective predictions at test time, leveraging each agent's pre-trained model.
arXiv Detail & Related papers (2021-06-22T18:29:58Z)
Counterfactual Representation Learning with Balancing Weights [74.67296491574318]
Key to causal inference with observational data is achieving balance in predictive features associated with each treatment type. Recent literature has explored representation learning to achieve this goal. We develop an algorithm for flexible, scalable and accurate estimation of causal effects.
arXiv Detail & Related papers (2020-10-23T19:06:03Z)
Counterfactual Predictions under Runtime Confounding [74.90756694584839]
We study the counterfactual prediction task in the setting where all relevant factors are captured in the historical data. We propose a doubly-robust procedure for learning counterfactual prediction models in this setting.
arXiv Detail & Related papers (2020-06-30T15:49:05Z)
A comprehensive study on the prediction reliability of graph neural networks for virtual screening [0.0]
We investigate the effects of model architectures, regularization methods, and loss functions on the prediction performance and reliability of classification results. Our result highlights that correct choice of regularization and inference methods is evidently important to achieve high success rate.
arXiv Detail & Related papers (2020-03-17T10:13:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.