Causal inference

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

G-Net: A Deep Learning Approach to G-computation for Counterfactual Outcome Prediction Under Dynamic Treatment Regimes (2020, methodology): “ is an inherently causal task in that it must account for the causal effects of following different treatment strategies. When treatment strategies of interest are time-varying, so-called “g-methods” [2, 3] are required to estimate their effects. G-methods include gcomputation [4–6], structural nest”
Double Reinforcement Learning for Efficient Off-Policy Evaluation in Markov Decision Processes (2019, methodology): “This is also often called the G-formula in the causal inference literature (Hernan and Robins, 2019; Robins, 1986).”
Can we believe the DAGs? A comment on the relationship between causal DAGs and mechanisms (2014, unknown): “2014–5:14pm] [1–21] //blrnas3/cenpro/ApplicationFiles/Journals/SAGE/3B2/SMMJ/Vol00000/130248/APPFile/SG-SMMJ130248.”
Ethical Machine Learning in Healthcare (2020, methodology): “e insucient to learn reliable models because features can have a mediating or moderating eect (post-treatment eect on outcomes of interest) and have to be incorporated dierently into model design (99). Modern ML and causal discovery techniques can identify sources of confounding at scale (100), alth”