4.5k citations · 7.2k across the 61 of their papers we have counts for
62 papers
End-to-End Balancing for Causal Continuous Treatment-Effect Estimation
Mohammad Taha Bahadori, Eric Tchetgen Tchetgen, David E. Heckerman
We study the problem of observational causal inference with continuous treatments in the framework of inverse propensity-score weighting. To obtain stable weights, we design a new…
Likelihoods and Parameter Priors for Bayesian Networks
David Heckerman, Dan Geiger
We develop simple methods for constructing likelihoods and parameter priors for learning about the parameters and structure of a Bayesian network. In particular, we introduce sever…
Parameter Priors for Directed Acyclic Graphical Models and the Characterization of Several Probability Distributions
Dan Geiger, David Heckerman
We develop simple methods for constructing parameter priors for model choice among Directed Acyclic Graphical (DAG) models. In particular, we introduce several assumptions that per…
Debiasing Concept-based Explanations with Causal Analysis
Mohammad Taha Bahadori, David E. Heckerman
Concept-based explanation approach is a popular model interpertability tool because it expresses the reasons for a model's predictions in terms of concepts that are meaningful for…
A Tutorial on Learning With Bayesian Networks
David Heckerman
A Bayesian network is a graphical model that encodes probabilistic relationships among variables of interest. When used in conjunction with statistical techniques, the graphical mo…
Embedded Bayesian Network Classifiers
David Heckerman, Chris Meek
Low-dimensional probability models for local distribution functions in a Bayesian network include decision trees, decision graphs, and causal independence models. We describe a new…