Learning Interpretable Models with Causal Guarantees

01/24/2019
by   Carolyn Kim, et al.
0

Machine learning has shown much promise in helping improve the quality of medical, legal, and economic decision-making. In these applications, machine learning models must satisfy two important criteria: (i) they must be causal, since the goal is typically to predict individual treatment effects, and (ii) they must be interpretable, so that human decision makers can validate and trust the model predictions. There has recently been much progress along each direction independently, yet the state-of-the-art approaches are fundamentally incompatible. We propose a framework for learning causal interpretable models---from observational data---that can be used to predict individual treatment effects. Our framework can be used with any algorithm for learning interpretable models. Furthermore, we prove an error bound on the treatment effects predicted by our model. Finally, in an experiment on real-world data, we show that the models trained using our framework significantly outperform a number of baselines.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
06/21/2022

Interpretable Deep Causal Learning for Moderation Effects

In this extended abstract paper, we address the problem of interpretabil...
research
04/24/2020

Methods for Individual Treatment Assignment: An Application and Comparison for Playlist Generation

We present a systematic analysis of causal treatment assignment decision...
research
07/12/2022

Revealing Unfair Models by Mining Interpretable Evidence

The popularity of machine learning has increased the risk of unfair mode...
research
08/18/2023

Causal Interpretable Progression Trajectory Analysis of Chronic Disease

Chronic disease is the leading cause of death, emphasizing the need for ...
research
11/05/2021

Distilling Heterogeneity: From Explanations of Heterogeneous Treatment Effect Models to Interpretable Policies

Internet companies are increasingly using machine learning models to cre...
research
02/25/2022

Ensemble Method for Estimating Individualized Treatment Effects

In many medical and business applications, researchers are interested in...

Please sign up or login with your details

Forgot password? Click here to reset