Who Are You?
These vignettes span the range of users that thinkCausal has been designed for. The tool allows you to visualize your data through a simple point-and-click interface, and includes numerous checks along the way to ensure assumptions are met, regardless of how familiar you are with statistics or causal inference.
Avery
"We didn't learn this in stats school!"
FamiliarityStatistics
Causal Inference
Core challenge: Including sensitivity analysis in models and ensuring correct model fit.
You may be: a statistics graduate in the workforce
Dakota
"Data Analyst in over their head"
FamiliarityStatistics
Causal Inference
Core challenge: Grasping basic causal inference principles and effectively communicating findings.
You may be: a Data Analyst in Public Policy
Kit
"Alphabet soup professor"
FamiliarityStatistics
Causal Inference
Core challenge: Leveraging advanced features of BART output and interpreting heterogeneous treatment effects.
You may be: an Assistant Professor in Public Health
Lindsey
"I'm not learning R!"
FamiliarityStatistics
Causal Inference
Core challenge: Overcoming resistance to new methodologies like BART due to preference for familiar tools.
You may be: an Economics Professor
Sidney
"I got this... I think?"
FamiliarityStatistics
Causal Inference
Core challenge: Selecting appropriate variables for analysis and understanding causal inference methods.
You may be: a program evaluator in social psychology
Taylor
"Is this a black box?"
FamiliarityStatistics
Causal Inference
Core challenge: Understanding and explaining the BART estimation process, comparing BART to other methods.
You may be: a researcher who has an MPH in epidemiology
