Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Is Causal Reasoning Harder than Probabilistic Reasoning?   Many tasks in statistical and causal inference can be construed as problems
of \emph{entailment} in a suitable formal language. We ask whether those
problems are more difficult, from a computational perspective, for
\emph{causal} probabilistic languages than for pure probabilistic (or
"associational") languages. Despite several senses in which causal reasoning is
indeed more complex -- both expressively and inferentially -- we show that
causal entailment (or satisfiability) problems can be systematically and
robustly reduced to purely probabilistic problems. Thus there is no jump in
computational complexity. Along the way we answer several open problems
concerning the complexity of well known probability logics, in particular
demonstrating the $\exists\mathbb{R}$-completeness of a polynomial probability
calculus, as well as a seemingly much simpler system, the logic of comparative
conditional probability.

26
The categories of this paper are: bayes-an
25
The categories of this paper are: patt-sol
8
The categories of this paper are: hep-ex
20
The categories of this paper are: acc-phys
12
The categories of this paper are: nucl-th
0
The categories of this paper are: hep-ph
4
The categories of this paper are: cond-mat
34
The categories of this paper are: chem-ph
17
The categories of this paper are: q-fin
3
The categories of this paper are: physics
27
The categories of this paper are: comp-gas
30
The categories of this paper are: q-alg
2
The categories of this paper are: math
7
The categories of this paper are: hep-th
24
The categories of this paper are: dg-ga
16
The categories of this paper are: stat