Neuroevolutionary Feature Representations for Causal Inference

05/21/2022
by   Michael C. Burkhart, et al.
0

Within the field of causal inference, we consider the problem of estimating heterogeneous treatment effects from data. We propose and validate a novel approach for learning feature representations to aid the estimation of the conditional average treatment effect or CATE. Our method focuses on an intermediate layer in a neural network trained to predict the outcome from the features. In contrast to previous approaches that encourage the distribution of representations to be treatment-invariant, we leverage a genetic algorithm that optimizes over representations useful for predicting the outcome to select those less useful for predicting the treatment. This allows us to retain information within the features useful for predicting outcome even if that information may be related to treatment assignment. We validate our method on synthetic examples and illustrate its use on a real life dataset.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
02/14/2019

Classifying Treatment Responders Under Causal Effect Monotonicity

In the context of individual-level causal inference, we study the proble...
research
08/29/2022

A Bayesian nonparametric approach for causal inference with multiple mediators

Mediation analysis with contemporaneously observed multiple mediators is...
research
10/27/2021

Doubly Robust Criterion for Causal Inference

The semiparametric estimation approach, which includes inverse-probabili...
research
02/22/2022

Learning Infomax and Domain-Independent Representations for Causal Effect Inference with Real-World Data

The foremost challenge to causal inference with real-world data is to ha...
research
03/16/2017

Causal Inference through the Method of Direct Estimation

The intersection of causal inference and machine learning is a rapidly a...
research
01/20/2023

Causal Inference under Data Restrictions

This dissertation focuses on modern causal inference under uncertainty a...
research
07/19/2022

Heterogeneous Treatment Effect with Trained Kernels of the Nadaraya-Watson Regression

A new method for estimating the conditional average treatment effect is ...

Please sign up or login with your details

Forgot password? Click here to reset