Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Stochastic Causal Programming for Bounding Treatment Effects   Causal effect estimation is important for numerous tasks in the natural and
social sciences. However, identifying effects is impossible from observational
data without making strong, often untestable assumptions. We consider
algorithms for the partial identification problem, bounding treatment effects
from multivariate, continuous treatments over multiple possible causal models
when unmeasured confounding makes identification impossible. We consider a
framework where observable evidence is matched to the implications of
constraints encoded in a causal model by norm-based criteria. This generalizes
classical approaches based purely on generative models. Casting causal effects
as objective functions in a constrained optimization problem, we combine
flexible learning algorithms with Monte Carlo methods to implement a family of
solutions under the name of stochastic causal programming. In particular, we
present ways by which such constrained optimization problems can be
parameterized without likelihood functions for the causal or the observed data
model, reducing the computational and statistical complexity of the task.

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The categories of this paper are: nlin
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The categories of this paper are: comp-gas
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The categories of this paper are: quant-ph
2
The categories of this paper are: math
36
The categories of this paper are: cmp-lg
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The categories of this paper are: funct-an
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The categories of this paper are: hep-ex
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The categories of this paper are: econ
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The categories of this paper are: q-fin
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The categories of this paper are: solv-int
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The categories of this paper are: math-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: supr-con
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The categories of this paper are: mtrl-th
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The categories of this paper are: ao-sci
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The categories of this paper are: patt-sol