What-If Reasoning with Counterfactual Gaussian Processes
Year: 2017
Type: preprint
Abstract: Answering What if? questions is important in many domains. For example,would a patient's disease progression slow down if I were to give them a dose
of drug A? Ideally, we answer our question using an experiment, but this is not
always possible (e.g., it may be unethical). As an alternative, we can use
non-experimental data to learn models that make counterfactual predictions of
what we would observe had we run an experiment. In this paper, we propose a
model to make counterfactual predictions about how continuous-time trajectories
(time series) respond to sequences of actions taken in continuous-time. We
develop our model within the potential outcomes framework of Neyman and Rubin.
One challenge is that the assumptions commonly made to learn potential outcome
(counterfactual) models from observational data are not applicable in
continuous-time as-is. We therefore propose a model using marked point
processes and Gaussian processes, and develop alternative assumptions that
allow us to learn counterfactual models from continuous-time observational
data. We evaluate our approach on two tasks from health care: disease
trajectory prediction and personalized treatment planning.
Source: arXiv (Cornell University)
Authors: Peter Schulam, Suchi Saria
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2605331588