1,789 research outputs found
Learning Counterfactual Representations for Estimating Individual Dose-Response Curves
Estimating what would be an individual's potential response to varying levels
of exposure to a treatment is of high practical relevance for several important
fields, such as healthcare, economics and public policy. However, existing
methods for learning to estimate counterfactual outcomes from observational
data are either focused on estimating average dose-response curves, or limited
to settings with only two treatments that do not have an associated dosage
parameter. Here, we present a novel machine-learning approach towards learning
counterfactual representations for estimating individual dose-response curves
for any number of treatments with continuous dosage parameters with neural
networks. Building on the established potential outcomes framework, we
introduce performance metrics, model selection criteria, model architectures,
and open benchmarks for estimating individual dose-response curves. Our
experiments show that the methods developed in this work set a new
state-of-the-art in estimating individual dose-response
Counterfactual Learning with Multioutput Deep Kernels
In this paper, we address the challenge of performing counterfactual
inference with observational data via Bayesian nonparametric regression
adjustment, with a focus on high-dimensional settings featuring multiple
actions and multiple correlated outcomes. We present a general class of
counterfactual multi-task deep kernels models that estimate causal effects and
learn policies proficiently thanks to their sample efficiency gains, while
scaling well with high dimensions. In the first part of the work, we rely on
Structural Causal Models (SCM) to formally introduce the setup and the problem
of identifying counterfactual quantities under observed confounding. We then
discuss the benefits of tackling the task of causal effects estimation via
stacked coregionalized Gaussian Processes and Deep Kernels. Finally, we
demonstrate the use of the proposed methods on simulated experiments that span
individual causal effects estimation, off-policy evaluation and optimization
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