Causal inference and biostatistics

July 11, 2009

(This article was first published on Realizations in Biostatistics, and kindly contributed to R-bloggers)

I’ve been following the discussion on causal inference over at Gelman’s blog with quite a bit of interest. Of course, this is in response to Judea Pearl’s latest book on causal inference, which differs quite a bit from the theory that had been forwarded by Donald Rubin and his colleagues for the last 35 years or so.

This is a theory that I think deserves more attention in biostatistics. After all, it goes back to the root of why we are studying drugs. Ultimately, we really don’t give a damn about whether outcomes are better in the treated group than in the placebo group. Rather, we are more interested in whether we are able to benefit individuals by giving them a treatment. In other words, we are interested in the unknowable quantity of what each person’s outcome is if they are treated and what it is if they are not. If there’s an improvement and it outweighs the (unknowable) risks, the drug is worth while. The reason we are interested in outcomes of a treated group and outcomes of a placebo group is that it’s a surrogate for this unknowable quantity, especially if you use the intention-to-treat principle. However, as mentioned in the linked article and the research by Rubin, the intention to treat principle fails to deliver on its promise despite its simplicity and popularity.

Some clinical trials are now being run with causal inference as a central part of the design. Tools such as R and WinBUGS and Bayesian concepts now make this logistically feasible. Further advances in statistical handling of partial compliance to treatment, biological pathways of drugs, and the intention to treat principle itself make causal inference look much more desirable by the day. It’s really only inertia caused by the popularity and (apparent) simplicity of intention to treat that makes this concept slower to catch on.

To leave a comment for the author, please follow the link and comment on their blog: Realizations in Biostatistics. offers daily e-mail updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...

If you got this far, why not subscribe for updates from the site? Choose your flavor: e-mail, twitter, RSS, or facebook...

Comments are closed.


Mango solutions

RStudio homepage

Zero Inflated Models and Generalized Linear Mixed Models with R

Quantide: statistical consulting and training


CRC R books series

Contact us if you wish to help support R-bloggers, and place your banner here.

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)