37 citations · 102 across the 10 of their papers we have counts for
10 papers
Improving the Efficiency of the PC Algorithm by Using Model-Based Conditional Independence Tests
Erica Cai, Andrew McGregor, David Jensen
Learning causal structure is useful in many areas of artificial intelligence, including planning, robotics, and explanation. Constraint-based structure learning algorithms such as…
Measuring Interventional Robustness in Reinforcement Learning
Katherine Avery, Jack Kenney, Pracheta Amaranath +2
Recent work in reinforcement learning has focused on several characteristics of learned policies that go beyond maximizing reward. These properties include fairness, explainability…
Bayesian causal inference via probabilistic program synthesis
Sam Witty, Alexander Lew, David Jensen +1
Causal inference can be formalized as Bayesian inference that combines a prior distribution over causal models and likelihoods that account for both observations and interventions.…
PlanAlyzer: Assessing Threats to the Validity of Online Experiments
Emma Tosch, Eytan Bakshy, Emery D. Berger +2
Online experiments are ubiquitous. As the scale of experiments has grown, so has the complexity of their design and implementation. In response, firms have developed software frame…
Evaluating Causal Models by Comparing Interventional Distributions
Dan Garant, David Jensen
The predominant method for evaluating the quality of causal models is to measure the graphical accuracy of the learned model structure. We present an alternative method for evaluat…
Causal Discovery for Manufacturing Domains
Katerina Marazopoulou, Rumi Ghosh, Prasanth Lade +1
Yield and quality improvement is of paramount importance to any manufacturing company. One of the ways of improving yield is through discovery of the root causal factors affecting…