text: 81,41,1419848836109856769,96779364,Arnab Bhattacharyya,"New paper: <LINK> (Efficient inference of interventional distributions) with Sutanu Gayen, @Saravanan_CU, Vedant Raval, and N.V. Vinodchandran.  TL;DR: Get observational samples, learn interventional distribution, with guarantees for poly # samples and runtime. Continuing a line of work we initiated earlier (<LINK>), we give finite sample guarantees for estimating causal effects, given knowledge of a causal graph and access to observational samples. All variables are discrete, from a finite-sized alphabet (e.g, binary). Seminal work by @yudapearl and Shpitser 15+ years ago had pinpointed conditions under which interventions are identifiable in this setting. They gave an algorithm which can require an unbounded number of samples. Our work makes their result algorithmic and quantitative. We also prove a hardness result showing that marginals of Bayes nets (no interventions) are hard to learn. That is, we can't expect a poly time algorithm that outputs a circuit which implements an approximate probability mass function for the marginal on n/2 nodes. #causality #causalinference #Statistics #epitwitter",https://arxiv.org/abs/2107.11712,"We consider the problem of efficiently inferring interventional distributions in a causal Bayesian network from a finite number of observations. Let $\mathcal{P}$ be a causal model on a set $\mathbf{V}$ of observable variables on a given causal graph $G$. For sets $\mathbf{X},\mathbf{Y}\subseteq \mathbf{V}$, and setting ${\bf x}$ to $\mathbf{X}$, let $P_{\bf x}(\mathbf{Y})$ denote the interventional distribution on $\mathbf{Y}$ with respect to an intervention ${\bf x}$ to variables ${\bf x}$. Shpitser and Pearl (AAAI 2006), building on the work of Tian and Pearl (AAAI 2001), gave an exact characterization of the class of causal graphs for which the interventional distribution $P_{\bf x}({\mathbf{Y}})$ can be uniquely determined. We give the first efficient version of the Shpitser-Pearl algorithm. In particular, under natural assumptions, we give a polynomial-time algorithm that on input a causal graph $G$ on observable variables $\mathbf{V}$, a setting ${\bf x}$ of a set $\mathbf{X} \subseteq \mathbf{V}$ of bounded size, outputs succinct descriptions of both an evaluator and a generator for a distribution $\hat{P}$ that is $\varepsilon$-close (in total variation distance) to $P_{\bf x}({\mathbf{Y}})$ where $Y=\mathbf{V}\setminus \mathbf{X}$, if $P_{\bf x}(\mathbf{Y})$ is identifiable. We also show that when $\mathbf{Y}$ is an arbitrary set, there is no efficient algorithm that outputs an evaluator of a distribution that is $\varepsilon$-close to $P_{\bf x}({\mathbf{Y}})$ unless all problems that have statistical zero-knowledge proofs, including the Graph Isomorphism problem, have efficient randomized algorithms. ",Efficient inference of interventional distributions,5,"['New paper: <LINK> (Efficient inference of interventional distributions) with Sutanu Gayen, @Saravanan_CU, Vedant Raval, and N.V. Vinodchandran. \n\nTL;DR: Get observational samples, learn interventional distribution, with guarantees for poly # samples and runtime.', 'Continuing a line of work we initiated earlier (https://t.co/ESygJG9Wkl), we give finite sample guarantees for estimating causal effects, given knowledge of a causal graph and access to observational samples. All variables are discrete, from a finite-sized alphabet (e.g, binary).', 'Seminal work by @yudapearl and Shpitser 15+ years ago had pinpointed conditions under which interventions are identifiable in this setting. They gave an algorithm which can require an unbounded number of samples.\n\nOur work makes their result algorithmic and quantitative.', ""We also prove a hardness result showing that marginals of Bayes nets (no interventions) are hard to learn. That is, we can't expect a poly time algorithm that outputs a circuit which implements an approximate probability mass function for the marginal on n/2 nodes."", '#causality #causalinference #Statistics #epitwitter']",21,07,1114