Estimating individual-level optimal causal interventions combining causal models and machine learning models - DSA ADS Course 2023

DSA ADS Course 2023

This DSA ADS course is part of a series of courses that demonstrate how to use applied data science with high performance compute and high quality data to optimize decision making in real world scenarios.

Discuss statistical causal inference methods.

Estimating individual-level optimal causal interventions combining causal models and machine learning models - 2021

Abstract

We introduce a new statistical causal inference method to estimate individual-level optimal causal intervention, that is, to which value we should set the value of a certain variable of an individual to obtain a desired value of another variable. This is defined as an optimization problem to minimize the error between a desired value and the value that would have been attained under the setting for the individual. To solve the optimization problem, we first train a machine learning model to predict the value of an objective variable and then estimate the causal structure of variables. We then combine the machine learning model and causal structure into a single causal model to estimate counterfactual value of the predicted objective variable. This is effective in achieving a more accurate estimation of individual-level optimal causal intervention. We further propose a gradient descent algorithm to compute the optimal causal intervention. Our method is generally applicable to continuous variables that are linearly and non-linearly related. In experiments, we evaluate the effectiveness of our method using artificial data generated by non-linear causal structures and real data.