Causal Inference
In health research, the questions that motivate most studies are interested in identifying causal relationships, not associational relationships. For example, what is the efficacy of a specific drug on a specific population? What was the cause of death of a given individual? Are certain environmental exposures harmful? What is the efficacy of new therapy X? These are all causal questions. Causal analysis is now well established, but requires extensions to the standard mathematical language of statistics. The following links will help guide the researcher through these concepts. Harvard Epi Group (mainly SAS macros): Software packages developed by Harvard Program on Causal Inference. This webpage contains implementation of various cutting edge statistical methods for the causal analysis of complex longitudinal data in epidemiology and its methodologically allied sciences, such as biostatistics, health services research, sociology, education, health and social behavior, economics, computer science, artificial intelligence, and philosophy. PENN DR estimators, IVs and stuff: Software packages developed by the Center for Causal Inference at Penn. This webpage contains implementation of various novel causal inference methods. Areas of focus include: instrumental variables; matching; mediation; Bayesian nonparametric models; semiparametric theory and methods; propensity scores; structural nested models; estimating optimal dynamic treatment strategies; and sensitivity analysis. NCSU some of those are causal, like dynamic treatment regime stuff: Software packages developed by Innovative Methods Program for Advancing Clinical Trials (IMPACT) by the University of North Carolina at Chapel Hill, Duke University and North Carolina State University. This webpage contains implementation of various statistical methods, with a large portion of causal inference analysis methods ranging from new trial designs and analysis methods that integrate biomarkers, analysis methods of existing data on biomarkers and outcomes to improve the design of future studies, pharmacogenomics for identifying biomarkers and candidate individualized therapies, and discovering and validating sequential, personalized decision-making strategies for cancer treatment. The github site for the tmleCommuniy package on targeted Maximum Likelihood Estimation (tMLE) for Hierarchical Data developed by the research group of Mark van der Laan at UC Berkeley. It performs tMLE of the average causal effect of community-based intervention(s) at a single time point on an individual-based outcome of interest. Implementations of the inverse-probability-of- treatment-weighting (IPTW) and the G-computation formula (GCOMP) are also available in the package. Python package for causal inference: Python package that implements various statistical and econometric methods used in the field variously known as Causal Inference, Program Evaluation, or Treatment Effect Analysis. A Crash Course in Good and Bad Control: A crash course in deciding which variables to control for in regression analysis using a causal inference framework.
Causal Inference
Our Office
Williams Building
University of Utah Research Park
Williams Building, 1st floor
295 South Chipeta Way
Salt Lake City, Utah
Map
Parking: During construction, you may park on the bottom floor of the south parking structure.
Contact
Camie Derricott
Camie.Derricott@hsc.utah.edu
Acknowledging the SDBC
Please use the following text to acknowledge the CTSI Study Design and Biostatistics Center:
"This investigation was supported by Translational Research: Implementation, Analysis and Design (TRIAD), with funding in part from the National Center for Advancing Translational Sciences of the National Institutes of Health under Award Number UM1TR004409. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health."
"This investigation was supported by the Study Design and Biostatistics Center (SDBC), with funding in part from the National Center for Advancing Translational Sciences of the National Institutes of Health under Award Number UM1TR004409. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health."