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A few weeks ago, one of my doctoral advisees wrote to me asking me how she could test for a specific mechanism behind the causal effect she is trying to estimate in her job-market paper.

Letting [math]y[/math] be her outcome of interest, [math]D[/math] her treatment of interest, [math]x[/math] be a vector of control variables, and [math]\epsilon[/math] be an error term with mean zero, my student was estimating

(1) [math]y = \alpha_{0} + \beta_{0}{x} + \gamma_{0}{D}+\epsilon[/math],

in which she was interested in [math]\gamma[/math], or the causal impact of [math]D[/math] on [math]y[/math].

But more importantly for the purposes of this post, she was also interested in whether [math]M[/math] is a mechanism through which [math]D[/math] causes [math]y[/math]. I suggested the usual thing I often see done, which is to estimate

(1′) [math]y = \alpha_{1} + \beta_{1}{x} + \phi_{1}{M} + \gamma_{1}{D}+\nu[/math],

in which case if [math]\gamma[/math] dropped out of significance and [math]\phi[/math] was significant (and had the “right”) sign, then she could say that [math]M[/math] was a mechanism through which [math]D[/math] cause [math]y[/math]. I also suggested maybe conducting a Davidson-MacKinnon J-test for non-nested hypotheses to assess the robustness of her mechanism finding.

Come gather ’round people
Wherever you roam
And admit that the waters
Around you have grown
And accept it that soon
You’ll be drenched to the bone
If your time to you is worth savin’
Then you better start swimmin’ or you’ll sink like a stone
For the times they are a-changin’

— Bob Dylan, “The Times They Are A-Changin’.”

On November 8, bucking the vast majority of polls, pundits, and prediction markets, Americans have elected Donald Trump as their president and have given him a majority of seats in both the Senate and the House of Representatives.

Whatever opinion I might have of the result of the election is immaterial, first because I am not a US citizen, and so I cannot vote in this country; and second, because I am not sure what the world needs at this point is more opinion.*

One thing I do have a cogent opinion on, however, is about the serious examination of conscience agricultural and applied economists need to do–especially left-leaning agricultural and applied economists.

First off, by “agricultural and applied economists,” I mean those of us who have any mix of research, teaching, and extension appointments in (what used to be known as) agricultural economics departments at land-grant universities. I know “applied economics” encompasses more than the category just delineated, but for the purposes of this discussion, I am choosing to go with the label the Agricultural and Applied Economics Association–my professional association–uses to designate people like my colleagues and me.

Jeffrey Smith and Arthur Sweetman have a very nice viewpoint article titled “Estimating the Causal Effects of Policies and Programs” in the latest issue of the Canadian Journal of Economics. The article is articulated around three points, viz. heterogeneity of treatment effects, the increased focus on internal validity over the past 20 years, and the use of economic theory to guide empirical work.

It is a good read–one that avoids taking some of the more extreme positions often taken by in that literature–and I plan on including it as a reading for the advanced econometrics course I teach every other year.

In reading Smith and Sweetman’s paper, I learned how to systematically think about selection into treatment when dealing with observational data. Their discussion can be particularly useful when you have survey data and your units of observation–in my case, that usually means individuals or households–are not randomly assigned to treatment but choose to participate on the basis of both their observable and unobservable characteristics, which means that you have to do the best you can with the data you have if you want to make a causal statement.