Learning Representations for Counterfactual Inference

05/12/2016
by   Fredrik D. Johansson, et al.
0

Observational studies are rising in importance due to the widespread accumulation of data in fields such as healthcare, education, employment and ecology. We consider the task of answering counterfactual questions such as, "Would this patient have lower blood sugar had she received a different medication?". We propose a new algorithmic framework for counterfactual inference which brings together ideas from domain adaptation and representation learning. In addition to a theoretical justification, we perform an empirical comparison with previous approaches to causal inference from observational data. Our deep learning algorithm significantly outperforms the previous state-of-the-art.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
12/09/2019

MetaCI: Meta-Learning for Causal Inference in a Heterogeneous Population

Performing inference on data obtained through observational studies is b...
research
10/01/2018

Perfect Match: A Simple Method for Learning Representations For Counterfactual Inference With Neural Networks

Learning representations for counterfactual inference from observational...
research
10/15/2020

Double Robust Representation Learning for Counterfactual Prediction

Causal inference, or counterfactual prediction, is central to decision m...
research
12/08/2021

Enhancing Counterfactual Classification via Self-Training

Unlike traditional supervised learning, in many settings only partial fe...
research
03/03/2023

Continual Causal Inference with Incremental Observational Data

The era of big data has witnessed an increasing availability of observat...
research
06/06/2023

Simulation-Based Counterfactual Causal Discovery on Real World Driver Behaviour

Being able to reason about how one's behaviour can affect the behaviour ...
research
06/15/2021

Contrastive Mixture of Posteriors for Counterfactual Inference, Data Integration and Fairness

Learning meaningful representations of data that can address challenges ...

Please sign up or login with your details

Forgot password? Click here to reset