Good questions. The history of causality in statistics is very complicated (partly due to the attitudes of big names like Fisher). There was one point not too long ago when people could not publish causality research in statistics journals as it was considered a “separate magisterium” (!). People who had something interesting to say about causality in statistics journals had to recast it as missing data problems.
All that is changing—somewhat. There were many many talks on causality at JSM this year, and the trend is set to continue. The set of people who is aware of what the g-formula is, or ignorability is, for example, is certainly much larger than 20 years ago.
As for what “proper causal analysis” is—there is some controversy here, and unsurprisingly the causal inference field splits up into camps (counterfactual vs not, graphs vs not, untestable assumptions vs not, etc.) It’s a bit like (http://xkcd.com/1095/).
Good questions. The history of causality in statistics is very complicated (partly due to the attitudes of big names like Fisher). There was one point not too long ago when people could not publish causality research in statistics journals as it was considered a “separate magisterium” (!). People who had something interesting to say about causality in statistics journals had to recast it as missing data problems.
All that is changing—somewhat. There were many many talks on causality at JSM this year, and the trend is set to continue. The set of people who is aware of what the g-formula is, or ignorability is, for example, is certainly much larger than 20 years ago.
As for what “proper causal analysis” is—there is some controversy here, and unsurprisingly the causal inference field splits up into camps (counterfactual vs not, graphs vs not, untestable assumptions vs not, etc.) It’s a bit like (http://xkcd.com/1095/).