2 citations · 7 across the 10 of their papers we have counts for
5 papers · 1 filter
Zero Inflation as a Missing Data Problem: a Proxy-based Approach
Trung Phung, Jaron J. R. Lee, Opeyemi Oladapo-Shittu +8
A common type of zero-inflated data has certain true values incorrectly replaced by zeros due to data recording conventions (rare outcomes assumed to be absent) or details of data…
A General Identification Algorithm For Data Fusion Problems Under Systematic Selection
Jaron J. R. Lee, AmirEmad Ghassami, Ilya Shpitser
Causal inference is made challenging by confounding, selection bias, and other complications. A common approach to addressing these difficulties is the inclusion of auxiliary data…
Ananke: A Python Package For Causal Inference Using Graphical Models
Jaron J. R. Lee, Rohit Bhattacharya, Razieh Nabi +1
We implement Ananke: an object-oriented Python package for causal inference with graphical models. At the top of our inheritance structure is an easily extensible Graph class that…
Markov-Restricted Analysis of Randomized Trials with Non-Monotone Missing Binary Outcomes
Jaron J. R. Lee, Agatha S. Mallett, Ilya Shpitser +3
Scharfstein et al. (2021) developed a sensitivity analysis model for analyzing randomized trials with repeatedly measured binary outcomes that are subject to nonmonotone missingnes…
Path Dependent Structural Equation Models
Ranjani Srinivasan, Jaron Lee, Rohit Bhattacharya +2
Causal analyses of longitudinal data generally assume that the qualitative causal structure relating variables remains invariant over time. In structured systems that transition be…