153 citations · 212 across the 15 of their papers we have counts for
4 papers · 1 filter
Bayesian causal inference for count potential outcomes
Young Lee, Wicher P. Bergsma, Marie-Abele C. Bind
The literature for count modeling provides useful tools to conduct causal inference when outcomes take non-negative integer values. Applied to the potential outcomes framework, we…
Additive interaction modelling using I-priors
Wicher Bergsma, Haziq Jamil
Additive regression models with interactions are widely studied in the literature, using methods such as splines or Gaussian process regression. However, these methods can pose cha…
Double Generative Adversarial Networks for Conditional Independence Testing
Chengchun Shi, Tianlin Xu, Wicher Bergsma +1
In this article, we study the problem of high-dimensional conditional independence testing, a key building block in statistics and machine learning. We propose an inferential proce…
Causal Modeling with Stochastic Confounders
Thanh Vinh Vo, Pengfei Wei, Wicher Bergsma +1
This work extends causal inference with stochastic confounders. We propose a new approach to variational estimation for causal inference based on a representer theorem with a rando…