no code implementations • NeurIPS 2020 • Aahlad Puli, Adler J. Perotte, Rajesh Ranganath
Causal inference relies on two fundamental assumptions: ignorability and positivity.
1 code implementation • NeurIPS 2020 • Mark Goldstein, Xintian Han, Aahlad Puli, Adler J. Perotte, Rajesh Ranganath
A survival model's calibration can be measured using, for instance, distributional calibration (D-CALIBRATION) [Haider et al., 2020] which computes the squared difference between the observed and predicted number of events within different time intervals.
no code implementations • 9 Jan 2020 • Amelia J. Averitt, Natnicha Vanitchanant, Rajesh Ranganath, Adler J. Perotte
Effect estimates, such as the average treatment effect (ATE), are then estimated as expectations under the reweighted or matched distribution, P .
no code implementations • NeurIPS 2011 • Adler J. Perotte, Frank Wood, Noemie Elhadad, Nicholas Bartlett
We introduce hierarchically supervised latent Dirichlet allocation (HSLDA), a model for hierarchically and multiply labeled bag-of-word data.
no code implementations • NeurIPS 2009 • Richard Socher, Samuel Gershman, Per Sederberg, Kenneth Norman, Adler J. Perotte, David M. Blei
We develop a probabilistic model of human memory performance in free recall experiments.