Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Probabilistic Analysis of Balancing Scores for Causal Inference   Propensity scores are often used for stratification of treatment and control
groups of subjects in observational data to remove confounding bias when
estimating of causal effect of the treatment on an outcome in so-called
potential outcome causal modeling framework. In this article, we try to get
some insights into basic behavior of the propensity scores in a probabilistic
sense. We do a simple analysis of their usage confining to the case of discrete
confounding covariates and outcomes. While making clear about behavior of the
propensity score our analysis shows how the so-called prognostic score can be
derived simultaneously. However the prognostic score is derived in a limited
sense in the current literature whereas our derivation is more general and
shows all possibilities of having the score. And we call it outcome score. We
argue that application of both the propensity score and the outcome score is
the most efficient way for reduction of dimension in the confounding covariates
as opposed to current belief that the propensity score alone is the most
efficient way.

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The categories of this paper are: q-fin
28
The categories of this paper are: alg-geom
37
The categories of this paper are: supr-con
36
The categories of this paper are: cmp-lg
31
The categories of this paper are: ao-sci
11
The categories of this paper are: hep-lat
1
The categories of this paper are: cs
23
The categories of this paper are: solv-int
7
The categories of this paper are: hep-th
12
The categories of this paper are: nucl-th
32
The categories of this paper are: atom-ph
0
The categories of this paper are: hep-ph
20
The categories of this paper are: acc-phys
26
The categories of this paper are: bayes-an
29
The categories of this paper are: funct-an
13
The categories of this paper are: quant-ph