36 research outputs found
Learning Optimal Biomarker-Guided Treatment Policy for Chronic Disorders
Electroencephalogram (EEG) provides noninvasive measures of brain activity
and is found to be valuable for diagnosis of some chronic disorders.
Specifically, pre-treatment EEG signals in alpha and theta frequency bands have
demonstrated some association with anti-depressant response, which is
well-known to have low response rate. We aim to design an integrated pipeline
that improves the response rate of major depressive disorder patients by
developing an individualized treatment policy guided by the resting state
pre-treatment EEG recordings and other treatment effects modifiers. We first
design an innovative automatic site-specific EEG preprocessing pipeline to
extract features that possess stronger signals compared with raw data. We then
estimate the conditional average treatment effect using causal forests, and use
a doubly robust technique to improve the efficiency in the estimation of the
average treatment effect. We present evidence of heterogeneity in the treatment
effect and the modifying power of EEG features as well as a significant average
treatment effect, a result that cannot be obtained by conventional methods.
Finally, we employ an efficient policy learning algorithm to learn an optimal
depth-2 treatment assignment decision tree and compare its performance with
Q-Learning and outcome-weighted learning via simulation studies and an
application to a large multi-site, double-blind randomized controlled clinical
trial, EMBARC
Doubly Robust Kernel Statistics for Testing Distributional Treatment Effects Even Under One Sided Overlap
As causal inference becomes more widespread the importance of having good
tools to test for causal effects increases. In this work we focus on the
problem of testing for causal effects that manifest in a difference in
distribution for treatment and control. We build on work applying kernel
methods to causality, considering the previously introduced Counterfactual Mean
Embedding framework (\textsc{CfME}). We improve on this by proposing the
\emph{Doubly Robust Counterfactual Mean Embedding} (\textsc{DR-CfME}), which
has better theoretical properties than its predecessor by leveraging
semiparametric theory. This leads us to propose new kernel based test
statistics for distributional effects which are based upon doubly robust
estimators of treatment effects. We propose two test statistics, one which is a
direct improvement on previous work and one which can be applied even when the
support of the treatment arm is a subset of that of the control arm. We
demonstrate the validity of our methods on simulated and real-world data, as
well as giving an application in off-policy evaluation.Comment: 9 pages, Preprin
A Neural Mean Embedding Approach for Back-door and Front-door Adjustment
We consider the estimation of average and counterfactual treatment effects,
under two settings: back-door adjustment and front-door adjustment. The goal in
both cases is to recover the treatment effect without having an access to a
hidden confounder. This objective is attained by first estimating the
conditional mean of the desired outcome variable given relevant covariates (the
"first stage" regression), and then taking the (conditional) expectation of
this function as a "second stage" procedure. We propose to compute these
conditional expectations directly using a regression function to the learned
input features of the first stage, thus avoiding the need for sampling or
density estimation. All functions and features (and in particular, the output
features in the second stage) are neural networks learned adaptively from data,
with the sole requirement that the final layer of the first stage should be
linear. The proposed method is shown to converge to the true causal parameter,
and outperforms the recent state-of-the-art methods on challenging causal
benchmarks, including settings involving high-dimensional image data