4 papers
Sinkhorn Treatment Effects: A Causal Optimal Transport Measure
Medha Agarwal, Alex Luedtke
We introduce the Sinkhorn treatment effect, an entropic optimal transport measure of divergence between counterfactual distributions. Unlike classical quantities such as the averag…
Quasi-Newton acceleration of EM and MM algorithms via Broydens method
Medha Agarwal, Jason Xu
The principle of majorization-minimization (MM) provides a general framework for eliciting effective algorithms to solve optimization problems. However, they often suffer from slow…
A principled stopping rule for importance sampling
Medha Agarwal, Dootika Vats, Víctor Elvira
Importance sampling (IS) is a Monte Carlo technique that relies on weighted samples, simulated from a proposal distribution, to estimate intractable integrals. The quality of the e…
Globally-centered autocovariances in MCMC
Medha Agarwal, Dootika Vats
Autocovariances are a fundamental quantity of interest in Markov chain Monte Carlo (MCMC) simulations with autocorrelation function (ACF) plots being an integral visualization tool…