I See, Therefore I Do: Estimating Causal Effects for Image Treatments

TL;DR

This paper introduces NICE, a novel model for estimating causal effects using image treatments, addressing a gap in handling high-dimensional treatment data like images, and demonstrating superior performance over existing methods.

Contribution

NICE is the first model to effectively incorporate high-dimensional image treatments for causal effect estimation, filling a significant gap in current methodologies.

Findings

NICE outperforms existing models in semi-synthetic experiments.

The proposed framework effectively utilizes rich image information.

NICE maintains robustness in zero-shot causal effect estimation.

Abstract

Causal effect estimation under observational studies is challenging due to the lack of ground truth data and treatment assignment bias. Though various methods exist in literature for addressing this problem, most of them ignore multi-dimensional treatment information by considering it as scalar, either continuous or discrete. Recently, certain works have demonstrated the utility of this rich yet complex treatment information into the estimation process, resulting in better causal effect estimation. However, these works have been demonstrated on either graphs or textual treatments. There is a notable gap in existing literature in addressing higher dimensional data such as images that has a wide variety of applications. In this work, we propose a model named NICE (Network for Image treatments Causal effect Estimation), for estimating individual causal effects when treatments are images. NICE demonstrates an effective way to use the rich multidimensional information present in image treatments that helps in obtaining improved causal effect estimates. To evaluate the performance of NICE, we propose a novel semi-synthetic data simulation framework that generates potential outcomes when images serve as treatments. Empirical results on these datasets, under various setups including the zero-shot case, demonstrate that NICE significantly outperforms existing models that incorporate treatment information for causal effect estimation.