21 citations · 43 across the 5 of their papers we have counts for
12 papers
Partial Identification with Noisy Covariates: A Robust Optimization Approach
Wenshuo Guo, Mingzhang Yin, Yixin Wang +1
Causal inference from observational datasets often relies on measuring and adjusting for covariates. In practice, measurements of the covariates can often be noisy and/or biased, o…
Point process models for sequence detection in high-dimensional neural spike trains
Alex H. Williams, Anthony Degleris, Yixin Wang +1
Sparse sequences of neural spikes are posited to underlie aspects of working memory, motor production, and learning. Discovering these sequences in an unsupervised manner is a long…
Towards Clarifying the Theory of the Deconfounder
Yixin Wang, David M. Blei
Wang and Blei (2019) studies multiple causal inference and proposes the deconfounder algorithm. The paper discusses theoretical requirements and presents empirical studies. Several…
The Blessings of Multiple Causes: A Reply to Ogburn et al. (2019)
Yixin Wang, David M. Blei
Ogburn et al. (2019, arXiv:1910.05438) discuss "The Blessings of Multiple Causes" (Wang and Blei, 2018, arXiv:1805.06826). Many of their remarks are interesting. But they also clai…
Multiple Causes: A Causal Graphical View
Yixin Wang, David M. Blei
Unobserved confounding is a major hurdle for causal inference from observational data. Confounders---the variables that affect both the causes and the outcome---induce spurious non…
Equal Opportunity and Affirmative Action via Counterfactual Predictions
Yixin Wang, Dhanya Sridhar, David M. Blei
Machine learning (ML) can automate decision-making by learning to predict decisions from historical data. However, these predictors may inherit discriminatory policies from past de…