4.5k citations · 7.2k across the 61 of their papers we have counts for
14 papers · 1 filter
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…
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…
Models and Selection Criteria for Regression and Classification
David Heckerman, Christopher Meek
When performing regression or classification, we are interested in the conditional probability distribution for an outcome or class variable Y given a set of explanatoryor input va…
Asymptotic Model Selection for Directed Networks with Hidden Variables
Dan Geiger, David Heckerman, Christopher Meek
We extend the Bayesian Information Criterion (BIC), an asymptotic approximation for the marginal likelihood, to Bayesian networks with hidden variables. This approximation can be u…