  We study the problem of identifying the causal relationship between two
discrete random variables from observational data. We recently proposed a novel
framework called entropic causality that works in a very general functional
model but makes the assumption that the unobserved exogenous variable has small
entropy in the true causal direction.
  This framework requires the solution of a minimum entropy coupling problem:
Given marginal distributions of m discrete random variables, each on n states,
find the joint distribution with minimum entropy, that respects the given
marginals. This corresponds to minimizing a concave function of nm variables
over a convex polytope defined by nm linear constraints, called a
transportation polytope. Unfortunately, it was recently shown that this minimum
entropy coupling problem is NP-hard, even for 2 variables with n states. Even
representing points (joint distributions) over this space can require
exponential complexity (in n, m) if done naively.
  In our recent work we introduced an efficient greedy algorithm to find an
approximate solution for this problem. In this paper we analyze this algorithm
and establish two results: that our algorithm always finds a local minimum and
also is within an additive approximation error from the unknown global optimum.
