Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Causal inference for ordinal outcomes   Many outcomes of interest in the social and health sciences, as well as in
modern applications in computational social science and experimentation on
social media platforms, are ordinal and do not have a meaningful scale. Causal
analyses that leverage this type of data, termed ordinal non-numeric, require
careful treatment, as much of the classical potential outcomes literature is
concerned with estimation and hypothesis testing for outcomes whose relative
magnitudes are well defined. Here, we propose a class of finite population
causal estimands that depend on conditional distributions of the potential
outcomes, and provide an interpretable summary of causal effects when no scale
is available. We formulate a relaxation of the Fisherian sharp null hypothesis
of constant effect that accommodates the scale-free nature of ordinal
non-numeric data. We develop a Bayesian procedure to estimate the proposed
causal estimands that leverages the rank likelihood. We illustrate these
methods with an application to educational outcomes in the General Social
Survey.

12
The categories of this paper are: nucl-th
28
The categories of this paper are: alg-geom
24
The categories of this paper are: dg-ga
31
The categories of this paper are: ao-sci
33
The categories of this paper are: plasm-ph
23
The categories of this paper are: solv-int
30
The categories of this paper are: q-alg
13
The categories of this paper are: quant-ph
17
The categories of this paper are: q-fin
37
The categories of this paper are: supr-con
34
The categories of this paper are: chem-ph
8
The categories of this paper are: hep-ex
21
The categories of this paper are: adap-org
25
The categories of this paper are: patt-sol
11
The categories of this paper are: hep-lat
18
The categories of this paper are: econ