9 citations · 36 across the 10 of their papers we have counts for
9 papers · 1 filter
Graph-based Complexity for Causal Effect by Empirical Plug-in
Rina Dechter, Annie Raichev, Alexander Ihler +1
This paper focuses on the computational complexity of computing empirical plug-in estimates for causal effect queries. Given a causal graph and observational data, any identifiable…
Estimating Causal Effects from Learned Causal Networks
Anna Raichev, Alexander Ihler, Jin Tian +1
The standard approach to answering an identifiable causal-effect query (e.g., ) when given a causal diagram and observational data is to first generate an estimand, or p…
Cutset Sampling with Likelihood Weighting
Bozhena Bidyuk, Rina Dechter
The paper analyzes theoretically and empirically the performance of likelihood weighting (LW) on a subset of nodes in Bayesian networks. The proposed scheme requires fewer samples…
Best-First AND/OR Search for Most Probable Explanations
Radu Marinescu, Rina Dechter
The paper evaluates the power of best-first search over AND/OR search spaces for solving the Most Probable Explanation (MPE) task in Bayesian networks. The main virtue of the AND/O…
AND/OR Multi-Valued Decision Diagrams (AOMDDs) for Weighted Graphical Models
Robert Mateescu, Rina Dechter
Compiling graphical models has recently been under intense investigation, especially for probabilistic modeling and processing. We present here a novel data structure for compiling…
Studies in Lower Bounding Probabilities of Evidence using the Markov Inequality
Vibhav Gogate, Bozhena Bidyuk, Rina Dechter
Computing the probability of evidence even with known error bounds is NP-hard. In this paper we address this hard problem by settling on an easier problem. We propose an approximat…