text: 124,90,1227573049488052225,96779364,Arnab Bhattacharyya,"New paper out! ""Efficiently learning and sampling interventional distributions from observations"" with Gayen, @Saravanan_CU, Maran and Vinodchandran: <LINK> #causalML #causalinference Causal inference is about predicting what happens in an imagined world that you don't have access to. E.g.: Am I more likely to get better if I take the medicine versus if I don't? How is the sale for product X affected if ads for X are slashed by 20%? @yudapearl and collaborators have thought long and hard about such questions. They realized that to properly formulate causal problems, one needs a model to describe how variables causally depend on each other. What they proposed is using Bayes nets to encode causal info. A basic question in this setup: given a Bayes net P on a set of variables, infer how the distribution would change if a particular variable is externally set (""intervened"") to a fixed value. @yudapearl & Jin Tian characterized the class of graphs for which this task is possible. What we do in our paper is make their result algorithmic. We show conditions (nearly tight) under which there are efficient algorithms (both in terms of samples & time) to infer the interventional distribution using samples from the observational distribution. TCS meets CI! Some thoughts from behind-the-curtain: reasoning about interventions is really very, very slippery! In particular, efficiently generating samples from the interventional distribution was unexpectedly quite tricky to do. Open problems! Efficient non-parametric algorithms for inferring interventions on several variables, estimating individual causal effects, estimating transportability error, etc. Can we do better if we add realistic parametric assumptions? Efficiently mitigating selection bias?",https://arxiv.org/abs/2002.04232,"We study the problem of efficiently estimating the effect of an intervention on a single variable (atomic interventions) using observational samples in a causal Bayesian network. Our goal is to give algorithms that are efficient in both time and sample complexity in a non-parametric setting. Tian and Pearl (AAAI `02) have exactly characterized the class of causal graphs for which causal effects of atomic interventions can be identified from observational data. We make their result quantitative. Suppose P is a causal model on a set $\vec{V}$ of n observable variables with respect to a given causal graph G with observable distribution $P$. Let $P_x$ denote the interventional distribution over the observables with respect to an intervention of a designated variable X with x. Assuming that $G$ has bounded in-degree, bounded c-components ($k$), and that the observational distribution is identifiable and satisfies certain strong positivity condition, we give an algorithm that takes $m=\tilde{O}(n\epsilon^{-2})$ samples from $P$ and $O(mn)$ time, and outputs with high probability a description of a distribution $\hat{P}$ such that $d_{\mathrm{TV}}(P_x, \hat{P}) \leq \epsilon$, and: 1. [Evaluation] the description can return in $O(n)$ time the probability $\hat{P}(\vec{v})$ for any assignment $\vec{v}$ to $\vec{V}$ 2. [Generation] the description can return an iid sample from $\hat{P}$ in $O(n)$ time. We also show lower bounds for the sample complexity showing that our sample complexity has an optimal dependence on the parameters $n$ and $\epsilon$, as well as if $k=1$ on the strong positivity parameter. ",Learning and Sampling of Atomic Interventions from Observations,7,"['New paper out! ""Efficiently learning and sampling interventional distributions from observations"" with Gayen, @Saravanan_CU, Maran and Vinodchandran: <LINK> #causalML #causalinference', ""Causal inference is about predicting what happens in an imagined world that you don't have access to. E.g.: Am I more likely to get better if I take the medicine versus if I don't? How is the sale for product X affected if ads for X are slashed by 20%?"", '@yudapearl and collaborators have thought long and hard about such questions. They realized that to properly formulate causal problems, one needs a model to describe how variables causally depend on each other. What they proposed is using Bayes nets to encode causal info.', 'A basic question in this setup: given a Bayes net P on a set of variables, infer how the distribution would change if a particular variable is externally set (""intervened"") to a fixed value. @yudapearl &amp; Jin Tian characterized the class of graphs for which this task is possible.', 'What we do in our paper is make their result algorithmic. We show conditions (nearly tight) under which there are efficient algorithms (both in terms of samples &amp; time) to infer the interventional distribution using samples from the observational distribution. TCS meets CI!', 'Some thoughts from behind-the-curtain: reasoning about interventions is really very, very slippery! In particular, efficiently generating samples from the interventional distribution was unexpectedly quite tricky to do.', 'Open problems! Efficient non-parametric algorithms for inferring interventions on several variables, estimating individual causal effects, estimating transportability error, etc. Can we do better if we add realistic parametric assumptions? Efficiently mitigating selection bias?']",20,02,1763