The essay is intended for a forthcoming handbook on political science methodology. I do think it makes points that would be of interest to social scientists more generally.

For some, it may seem obvious that social scientists should be working on clearly-defined societal problems. In political science, however, this is not how many people think about their research. Rather, the idea of “disinterested” or “agenda-free” pursuit of “explanation” and “puzzle solving” is very common, perhaps dominant. In my view, such an approach makes research largely an aesthetic exercise where judgments about research quality are driven by idiosyncratic tastes. That is just not the way I think about what I do, and frankly, if such a “disinterested” approach were to define our discipline, I’d have a hard time explaining why anyone should devote serious resources to it. There are much more compelling ways to meditate on the exercise of power and the human condition than “disinterested” regression studies in a political science journal.

The essay argues that taking a “problem-solving” mindset can help to organize one’s thinking about methodological questions. I propose that a problem-solving research program operates through three steps: (1) problem definition and description, (2) primarily observational examination of mechanisms that perpetuate the problem, and (3) primarily experimental studies to test intervention strategies to mitigate the problem. Social scientists should develop skills to operate through each of these stages, although some specialization in one of the phases makes perfect sense. Social science journals should devote nearly equal space to each of these types of research.

I have organized my teaching, advising, and assessments of research on the basis of this mindset. I think it is very powerful, and it helps me to address questions of priority in a systematic way. I think that students find it clarifying too.

My thinking is strongly influenced by recent contributions by Duflo ([link]) in economics and Moynihan ([link]) in public administration. I highly recommend these.

I would love to know what you think.

]]>The way I teach it is like this:

- Start with the DAG that represents the actual data-generating process (DGP).
- Next define a “target intervention graph” that represents, in DAG form, an ideal experimental DGP for the causal effects that you want to identify.
- Apply the graph moralization rules per Greenland and Pearl to check the implications of conditioning on different variables in the DAG for the actual DGP.
- You have identified a sufficient conditioning set when you have gotten the actual DGP DAG to look like the target intervention graph through conditioning and moralization. Note that for any given problem, there may be more than one conditioning set that is sufficient.

The graph moralization rules are as follows, quoting Greenland and Pearl:

*
Conditioning on a variable C in a DAG can be represented by creating a new graph from the original graph to represent constraints on relations within levels (strata) of C implied by the constraints imposed by the original graph. This conditional graph can be found by the following sequence of operations, sometimes called graphical moralization.*

- If C is a collider, join (marry) all pairs of parents of C by undirected arcs.
- Similarly, if A is an ancestor of C and a collider, join all pairs of parents of A by undirected arcs.
- Erase C and all arcs connecting C to other variables.

*(Greenland and Pearl, 2017, pp. 3-4)
*

Recently, @analisereal posed an identification conditioning problem:

In the spirit of the Crash Course, here’s another puzzle, brought to you by Ema Perkovic. Suppose you want to identify the total effect of a joint intervention of X1 and X2 on Y. Which variables should you include in you regression equation? U is unobserved. Quiz in next tweet. https://t.co/XFSYgozc3f pic.twitter.com/2Td7TeqXMl

— Análise Real (@analisereal) April 10, 2022

We can apply the recipe outlined above. The DAG in the tweet represents the actual DGP. The target intervention graph needs to represent “a joint intervention of X1 and X2 on Y.” This would be as follows:

To see what happens when we condition on the available controls, we would apply the graph moralization rules. Conditioning on Z2 would require that we apply rules 1 and 3, yielding: So we are not quite there with respect to our target graph. That said, the graph that results here is interesting, because it does capture a DGP that characterizes two conditionally independent effects of X1 and X2 on Y, and thus it does capture the effects of a joint intervention of X1 and X2 on Y in circumstances in which Z2 is held fixed. It’s just that the mediation pathway between X1 and Y is obscured relative to our target graph.

Conditioning only on Z1 (and not Z2) would require that we apply rule 3, yielding: Here, we have a DGP that is clean for the effect of X1 on Y, but the effect of X2 on Y is confounded by a backdoor path.

Conditioning on both Z1 and Z2 results in the following: The variable U is exogenous, and so we can remove it from the graph. This gets us to our target intervention graph, and represents a solution to the problem.

Now, when effects are heterogeneous with respect to conditioning variables, then we should have a way to remind ourselves that we need to marginalize conditional effect estimates over values of the conditioning variables. This would be necessary in order to get to a population-level estimate of the effects on the target intervention graph. The way I like to do it is to write the conditioning arguments next to the conditional graph, like this: Writing out “Z1=z1, Z2=z2” by the graph makes it clear that these are conditional relationships on the actual DGP, and that marginalization (with respect to z1 and z2) would be needed to get from this to the effects that target intervention graph represents in the population.

]]>Suppose we want to situate a scholar in their field, for example as part of a tenure review case. One way to do that is to look at the scholar’s papers and see who they are citing:

Go to their Google scholar profile and pull up their papers. Choose some of their most cited papers (reflecting how others see the scholar’s contributions) and some of their most recent papers (reflecting their current thinking).

Construct the network of people that the scholar references in their most prominent work.

A low-tech way to do this is to copy/paste bibliographies from the papers into https://anystyle.io/ to put the bibliographies into machine readable format (e.g., bibtex). I like to tag the entries from each paper’s bibliography by the date of the paper’s publication (e.g., by adding a custom field to the bibtex file) so that I can sort and see how the scholar’s reference base has changed over time. Compile the different bibliographies into a library in a reference manager. If you keep duplicate entries you get a sense of the scholar’s key points of reference.

A higher-tech way to do this is to use the Connected Papers app. You can look at the graph to find well-cited work that the scholar tends to reference.

UPDATE (12/14/23): The “InfluenceMap” project allows for creating an influence diagram (people that the scholar draws up, and then people who cite the scholar): [link]

- Pare down the list to seminal contributions. E.g., keep only entries from relevant general interest and field journals that are highly cited.

Now some analyses:

First, who appears most often in the library? What does the work of these primary referents represent in the literature and how does the current scholar’s work relate?

Second, whose work is being referenced at different times over the course of the scholar’s career (I do this using the custom field described above)? What does this say about how the scholar’s work has evolved alongside the reference literature?

As far as I know, the steps above are not as well-automated as methods to see who else is citing the scholar’s work (there are numerous tools to do that, like the “scholar” package in R). Would love to see someone do it (and welcome any suggestions below).

]]>I used to think economists used the idea of statistical discrimination to understand how discrimination could be defeated – by info vs some other means. But it seems like instead some think of it as a justification – a reason discrimination is OK. Sometimes economists bum me out.

— Sarah Jacobson (@SarahJacobsonEc) May 18, 2021

I have collected references to some of the papers that discussants mentioned as providing more refined takes on the original Arrow and Aigner-Cain analyses:

- Lundberg, Shelly J., and Richard Startz. “Private discrimination and social intervention in competitive labor market.” The American Economic Review 73.3 (1983): 340-347.
- Schwab, Stewart. “Is statistical discrimination efficient?.” The American Economic Review 76.1 (1986): 228-234.
- Coate, Stephen, and Glenn C. Loury. “Will affirmative-action policies eliminate negative stereotypes?.” The American Economic Review (1993): 1220-1240.
- Bohren, J. Aislinn, et al. Inaccurate statistical discrimination. No. w25935. National Bureau of Economic Research, 2019.
- Lang, Kevin, and Ariella Kahn-Lang Spitzer. “Race discrimination: An economic perspective.” Journal of Economic Perspectives 34.2 (2020): 68-89.
- Komiyama, Junpei, and Shunya Noda. “On Statistical Discrimination as a Failure of Social Learning: A Multi-Armed Bandit Approach.” arXiv preprint arXiv:2010.01079 (2020).
- Fosgerau, Mogens and Sethi, Rajiv and Weibull, Jorgen W., Costly Screening and Categorical Inequality (April 21, 2021). Available at SSRN: https://ssrn.com/abstract=3533952 or http://dx.doi.org/10.2139/ssrn.3533952

In settings with complex spatial effects and interference, the paper defines a type of marginal effect, the “average marginalized response,” that has a clear interpretation and can be identified with a spatial experiment and a simple contrast.

It took time to work out details for robust inference, and finally got there with Ye working out reasonable conditions that justify the spatial HAC variance estimator, and then by connecting to a breakthrough CLT result from Ogburn et al. (2020; arxiv link).

We are working on the public release of the R package and also a more didactic paper that walks through applications. Stay tuned for those.

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