Robust causal inference
using directed acyclic graphs: the R package 'dagitty'.
We've been using computers to make political predictions for years--so what will programming computers to understand causal inference
certainly benefit from the application of optimal methods and multi-institutional consortium-based efforts, but placing findings in the context of all the available evidence is surely at the heart of any scientific enterprise (12).
from randomized trials in social epidemiology.
Our study was strengthened by using nationally representative data, applying causal inference
methods, and distinguishing the effects of gaining and losing medical homes.
Designing a Causal Inference
System to Tune User Preferences
Among other advantages, sustained fieldwork presents the opportunity to use process tracing to bolster causal inference
(see Bennett and Checkel 2015; Dunning 2015).
Dissociating Processes Supporting Causal Perception and Causal Inference
in the Brain.
While a realistic nonstationary treatment of this case study is beyond our scope, it is important to underline that including assumptions of nonstationarity into a causal inference
study presents no particular difficulties in general.
Designing and implementing the simplest of research designs that support causal inference
is a daunting task.
His discussion of distinct matter focuses on noncausal operations that are permanent, irresistible, and universal (Treatise 220.127.116.11), showing that the belief in causal inference
and the belief in distinct matter cannot both be defeasibly justified and that the reliability theory of justification explains why only the belief in causal inference
can be defeasibly justified.
Econometrics, on the other hand, has focused on causal inference
from its very early days.