Survival analysis, longitudinal analysis and causal inference

A general implementation of TMLE for longitudinal data applied to causal inference in survival analysis.

In many randomized controlled trials the outcome of interest is a time to event, and one measures on each subject baseline covariates and time-dependent covariates until the subject either drops-out, the time to event is observed, or the end of study is reached. The goal of such a study is to assess the causal effect of the treatment on the survival curve. We present a targeted maximum likeliho...

response articles: micro and macro analysis

the present study reports an analysis of response articles in four different disciplines in the social sciences, i.e., linguistics, english for specific purposes (esp), accounting, and psychology. the study has three phases: micro analysis, macro analysis, and e-mail interview. the results of the micro analysis indicate that a three-level linguistic pattern is used by the writers in order to cr...

Counterfactuals, Causal Inference, and Historical Analysis

For Objective Causal Inference, Design Trumps Analysis

For obtaining causal inferences that are objective, and therefore have the best chance of revealing scientific truths, carefully designed and executed randomized experiments are generally considered to be the gold standard. Observational studies, in contrast, are generally fraught with problems that compromise any claim for objectivity of the resulting causal inferences. The thesis here is that...

Causal Inference in Time Series Analysis

The identification of causal relationships is an important part of scientific research and essential for understanding the consequences when moving from empirical findings to actions. At the same time, the notion of causality has shown to be evasive when trying to formalize it. Among the many properties a general definition of causality should or should not have, there are two important aspects...