179 citations · 398 across the 42 of their papers we have counts for
5 papers · 1 filter
Challenges in Obtaining Valid Causal Effect Estimates with Machine Learning Algorithms
Ashley I Naimi, Alan E Mishler, Edward H Kennedy
Unlike parametric regression, machine learning (ML) methods do not generally require precise knowledge of the true data generating mechanisms. As such, numerous authors have advoca…
Discussion of "Data-driven confounder selection via Markov and Bayesian networks" by Jenny Häggström
Edward H. Kennedy, Sivaraman Balakrishnan
In this discussion we consider why it is important to estimate causal effect parameters well even they are not identified, propose a partially identified approach for causal infere…
Semiparametric theory
Edward H. Kennedy
In this paper we give a brief review of semiparametric theory, using as a running example the common problem of estimating an average causal effect. Semiparametric models allow at…
Paradoxes in instrumental variable studies with missing data and one-sided noncompliance
Edward H. Kennedy, Dylan S. Small
It is common in instrumental variable studies for instrument values to be missing, for example when the instrument is a genetic test in Mendelian randomization studies. In this pap…
Nonparametric causal effects based on incremental propensity score interventions
Edward H. Kennedy
Most work in causal inference considers deterministic interventions that set each unit's treatment to some fixed value. However, under positivity violations these interventions can…