Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Causal Machine Learning Framework for Predicting Preventable Hospital
  Readmissions   Clinical predictive algorithms are increasingly being used to form the basis
for optimal treatment policies--that is, to enable interventions to be targeted
to the patients who will presumably benefit most. Despite taking advantage of
recent advances in supervised machine learning, these algorithms remain, in a
sense, blunt instruments--often being developed and deployed without a full
accounting of the causal aspects of the prediction problems they are intended
to solve. Indeed, in many settings, including among patients at risk of
readmission, the riskiest patients may derive less benefit from a preventative
intervention compared to those at lower risk. Moreover, targeting an
intervention to a population, rather than limiting it to a small group of
high-risk patients, may lead to far greater overall utility if the patients
with the most modifiable (or preventable) outcomes across the population could
be identified. Based on these insights, we introduce a causal machine learning
framework that decouples this prediction problem into causal and predictive
parts, which clearly delineates the complementary roles of causal inference and
prediction in this problem. We estimate treatment effects using causal forests,
and characterize treatment effect heterogeneity across levels of predicted risk
using these estimates. Furthermore, we show how these effect estimates could be
used in concert with the modeled "payoffs" associated with successful
prevention of individual readmissions to maximize overall utility. Based on
data taken from before and after the implementation of a readmissions
prevention intervention at Kaiser Permanente Northern California, our results
suggest that nearly four times as many readmissions could be prevented annually
with this approach compared to targeting this intervention using predicted
risk.

9
The categories of this paper are: nlin
2
The categories of this paper are: math
29
The categories of this paper are: funct-an
5
The categories of this paper are: gr-qc
7
The categories of this paper are: hep-th
27
The categories of this paper are: comp-gas
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The categories of this paper are: math-ph
11
The categories of this paper are: hep-lat
35
The categories of this paper are: mtrl-th
4
The categories of this paper are: cond-mat
21
The categories of this paper are: adap-org
30
The categories of this paper are: q-alg
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The categories of this paper are: nucl-th
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The categories of this paper are: nucl-ex
1
The categories of this paper are: cs
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The categories of this paper are: atom-ph