
Data science is science's second chance to get causal inference right: A classification of data science tasks
Causal inference from observational data is the goal of many health and ...
read it

A Primer on Causality in Data Science
Many questions in Data Science are fundamentally causal in that our obje...
read it

Causal Inference with Corrupted Data: Measurement Error, Missing Values, Discretization, and Differential Privacy
Even the most carefully curated economic data sets have variables that a...
read it

Targeted Learning with Daily EHR Data
Electronic health records (EHR) data provide a cost and timeeffective o...
read it

Targeted VAE: Structured Inference and Targeted Learning for Causal Parameter Estimation
Undertaking causal inference with observational data is extremely useful...
read it

Doing Things Twice: Strategies to Identify Studies for Targeted Validation
The "reproducibility crisis" has been a highly visible source of scienti...
read it

Using Longitudinal Targeted Maximum Likelihood Estimation in Complex Settings with Dynamic Interventions
Longitudinal targeted maximum likelihood estimation (LTMLE) has hardly e...
read it
Targeting Learning: Robust Statistics for Reproducible Research
Targeted Learning is a subfield of statistics that unifies advances in causal inference, machine learning and statistical theory to help answer scientifically impactful questions with statistical confidence. Targeted Learning is driven by complex problems in data science and has been implemented in a diversity of realworld scenarios: observational studies with missing treatments and outcomes, personalized interventions, longitudinal settings with timevarying treatment regimes, survival analysis, adaptive randomized trials, mediation analysis, and networks of connected subjects. In contrast to the (mis)application of restrictive modeling strategies that dominate the current practice of statistics, Targeted Learning establishes a principled standard for statistical estimation and inference (i.e., confidence intervals and pvalues). This multiply robust approach is accompanied by a guiding roadmap and a burgeoning software ecosystem, both of which provide guidance on the construction of estimators optimized to best answer the motivating question. The roadmap of Targeted Learning emphasizes tailoring statistical procedures so as to minimize their assumptions, carefully grounding them only in the scientific knowledge available. The end result is a framework that honestly reflects the uncertainty in both the background knowledge and the available data in order to draw reliable conclusions from statistical analyses  ultimately enhancing the reproducibility and rigor of scientific findings.
READ FULL TEXT
Comments
There are no comments yet.