Economic imperialism and causal inference
And I, for one, welcome our new economist overlords…
Readers not in academic social science may take the title of this post as indicating I’m writing about the use of economic might to imperialist ends.1 Rather, economic imperialism is a practice of economists (and acolytes) in which they invade research territories that traditionally “belong” to other social scientific disciplines.2 See this comic for one way you can react to this.3
Economists bring their theoretical, statistical, and research-funding resources to bear on problems that might not be considered economics. For example, freakonomists like Levitt study sumo wrestlers and the effects of the legalization of abortion on crime. But, hey, if the Commerce Clause means that Congress can legislate everything, then, for the same reasons, economists can — no, must — study everything.
I am not an economist by training, but I have recently had reason to read quite a bit in econometrics. Overall, I’m impressed.4 Economists have recently taken causal inference — learning about cause and effect relationships, often from observational data — quite seriously. In the eyes of some, this has precipitated a “credibility revolution” in economics. Certainly, papers in economics and (especially) econometrics journals consider threats to the validity of causal inference at length.
On the other hand, causal inference in the rest of the social sciences is simultaneously over-inhibited and under-inhibited. As Judea Pearl observes in his book Causality, lack of clarity about statistical models (that social scientists often don’t understand) and causality has induced confusion about distinctions between statistical and causal issues (i.e., between estimation methods and identification).5
So, on the one had, many psychologists stick to experiments. Randomized experiments are, generally, the gold standard for investigating cause–effect relationships, so this can and often does go well. However, social psychologists have recently been obsessed with using “mediation analysis” to investigate the mechanisms by which causes they can manipulate produce effects of interest. Investigators often manipulate some factors experimentally and then measure one or more variables they believe fully or partially mediate the effect of those factors on their outcome. Then, under the standard Baron & Kenny approach, psychologists fit a few regression models, including regressing the outcome on both the experimentally manipulated variables and the simply measured (mediating) variables. The assumptions required for this analysis to identify any effects of interest are rarely satisfied (e.g., effects on individuals are homogenous).6 So psychologists are often over-inhibited (experiments only please!) and under-inhibited (mediation analysis).
Likewise, in more observational studies (in psychology, sociology, education, etc.), investigators are sometimes wary of making explicit causal claims. So instead of carefully stating the causal assumptions that would justify different causal conclusions, readers are left with phrases like “suggests” and “is consistent with” followed by causal claims. Authors then recommend that further research be conducted to better support these causal conclusions. With these kinds of recommendations awaiting, no wonder that economists find the territory ready for taking: they can just show up with econometrics tools and get to work on hard-won questions that “rightly belong to others”.
- Well, if economists have better funding sources, this might apply in some sense. [↩]
- For arguments in favor of economic imperialism, see Lazear, E.P. (1999). Economic imperialism. NBER Working Paper No. 7300. [↩]
- Or see this comic for imperialism by physicists. [↩]
- At least by the contemporary literature on what I’ve been reading on — IVs, encouragement designs, endogenous interactions, matching estimators. But it is true that in some of these areas econometrics has been able to fruitfully borrow from work on potential outcomes in statistics and epidemiology. [↩]
- Econometricians have made similar observations. [↩]
- For a bit on this topic, see the discussion and links to papers here. [↩]
Homophily and peer influence are messy business
Some social scientists have recently been getting themselves into trouble (and limelight) claiming that they have evidence of direct and indirect “contagion” (peer influence effects) in obesity, happiness, loneliness, etc. Statisticians and methodologists — and even science journalists — have pointed out their troubles. In observational data, peer influence effects are confounded with those of homophily and common external causes. That is, people are similar to other people in their social neighborhood because ties are more likely to form between similar people, and many external events that could cause the outcome are localized in networks (e.g., fast food restaurant opens down the street).
Econometricians1 have worked out the conditions necessary for peer influence effects to be identifiable.2 Very few studies have plausibly satisfied these requirements. But even if an investigator meets these requirements, it is worth remembering that homophily and peer influence are still tricky to think about — let along produce credible quantitative estimates of.
As Andrew Gelman notes, homophily can depend on network structure and information cascades (a kind of peer influence effect) to enable the homophilous relationships to form. Likewise, the success or failure of influence in a relationship can affect that relationship. For example, once I convert you to my way of thinking — let’s say, about climate change, we’ll be better friends. To me, it seems like some of the downstream consequences of our similarity should be attributed to peer influence. If I get fat and so you do, it could be peer influence in many ways: maybe that’s because I convinced you that owning a propane grill is more environmentally friendly (and then we both ended up grilling a lot more red meat). Sounds like peer influence to me. But it’s not that me getting fat caused you to.
Part of the problem here is looking only at peer influence effects in a single behavior or outcome at once. I look forward to the “clear thinking and adequate data” (Manski) that will allow us to better understand these processes in the future. Until then: scientists, please at least be modest in your claims and radical policy recommendations. This is messy business.
- They do statistics but speak a different language than big “S” statisticians — kind of like machine learning folks. [↩]
- For example, see Manski, C. F. (2000). Economic analysis of social interactions. Journal of Economic Perspectives, 14(3):115–136. Economists call peer influence effects endogenous interactions and contextual interactions. [↩]
“Discovering Supertaskers”: Challenges in identifying individual differences from behavior
Some new research from the University of Utah suggests that a small fraction of the population consists of “supertaskers” whose performance is not reduced by multitasking, such as when completing tasks on a mobile phone while driving.
“Supertaskers did a phenomenal job of performing several different tasks at once,” Watson says. “We’d all like to think we could do the same, but the odds are overwhelmingly against it.” (Wired News & Science News)
The researchers, Watson and Strayer, argue that they have good evidence for the existence of this individual variation. One can find many media reports of this “discovery” of “supertaskers” (e.g., Psychology Today). I do not think this conclusion is well justified.
First, let’s consider the methods used in this research. 100 college students each completed driving tasks and an auditory task on a mobile phone — separately and in combination — over a single 1.5 hour session. The auditory task is designed to measure differences in executive attention by requiring participants do hold past items in memory while completing math tasks. The researchers identified “supertaskers” as those participants who met the following “stringent” requirements: they were both (a) in the top 25% of participants in performance in the single-task portions and (b) and not different in their dual-task performance on at least three of the four measures by more than the standard error. Since two of the four measures are associated with each of the two tasks (driving: brake reaction time, following distance; mobile phone task: memory performance, math performance), this requires that ”supertaskers” do as well on both measures of either the driving or mobile phone task and one measure of the other task.
There may be many issues with the validity of the inference in this work. I want to focus on one in particular: the inference from the observation of differences between participants’ performance in a single 1.5 hour session to the conclusion that there are stable, “trait” differences among participants, such that some are “supertaskers”. This conclusion is simply not justified. To illustrate this, let’s consider how the methods of this study differ from those usually (and reasonably) used by psychologists to reach such conclusions.
Psychologists often study individual differences using the following approach. First, identify some plausible trait of individuals. Second, construct a questionnaire or other (perhaps behavioral) test that measures that trait. Third, demonstrate that this test has high reliability — that is, that the differences between people are much larger than the differences between the same person taking the test at different times. Fourth, then use this test to measure the trait and see if it predicts differences in some experiment. A key point here is that in order to conclude that the test measures a stable individual difference (i.e., a trait) researchers need to establish high test-retest reliability; otherwise, the test might just be measuring differences in temporary mood.
Returning to Watson and Strayer’s research, it is easy to see the problem: we have no idea whether the variation observed should be attributed to stable individual differences (i.e., being a “supertasker”) or to unstable differences. That is, if we brought those same “supertasker” participants back into the lab and they did another session, would they still exhibit the same lack of performance difference between the single- and dual-task conditions? This research gives us no reason that expect that they would.
Watson and Strayer do some additional analysis with the aim of ruling out their observations being a fluke. One might think this addresses my criticism, but it does not. They
performed a Monte Carlo simulation in which randomly selected single-dual task pairs of variables from the existing data set were obtained for each of the 4 dependent measures and then subjected to the same algorithm that was used to classify the supertaskers.
That is, they broke apart the single-task and dual-task data for each participant and created new simulated participants by randomly sampling pairs single- and dual-task data. They found that on this analysis there would be only 1/15th of the observed ”supertaskers”. This is a good analysis to do. However, this just demonstrates that being labeled a “supertasker” is likely caused by the single- and dual-task data being generated by the same person in the same session. This stills leaves it quite open (and more plausible to me) that participants’ were in varying states for the session and this explains their (temporary) “supertasking”. It also allows that this greater frequency of “supertaskers” is due to participants who do well in whatever task they are given first being more likely to do well in subsequent tasks.
My aim in this post is to suggest some challenges that this kind of approach has to face. Part of my interest in this is that I’m quite sympathetic to identifying stable, observed differences in behavior and then “working backwards” to characterizing the traits that explain these downstream differences. This exactly the approach that Maurits Kaptein and I are taking in our work on persuasion profiling: we observe how individuals respond to the use of different influence strategies and use this to (a) construct a “persuasion profile” for that individual and (b) characterize how much variation in the effects of these strategies there is in the population.
However, a critical step in this process is ruling out the alternative explanation that the observed differences are primarily due to differences in, e.g., mood, rather than stable individual differences. One way to do this is to observe the behavior in multiple sessions and multiple contexts. Another way to rule out this alternative explanation is if you observe a complex pattern of behavioral differences that previous work suggests could not be the result of temporary, unstable differences — or at least is more easily explained by previous theories about the relevant traits. That is, I’m enthusiastic about identifying stable, observed differences in behavior, but I don’t want to see researchers abandon the careful methods that have been used in the past to make the case for a new individual difference.
Watson, Strayer, and colleagues have apparently begun doing work that could be used to show the stability of the observed differences. The discussion section of their paper refers to some additional unpublished research in which they invited their “supertaskers” from this study and another study back into the lab and had them do some similar tasks measuring executive attention (but not driving) while in an fMRI machine. They report greater “coherence” in their performance in this second study and the previous study than control participants and better performance for “supertaskers” on dual-N-back tasks. But this is short of showing high test-retest reliability.
Since little is said about this work, I hesitate to conclude anything from it or criticize it. I’ve contacted the authors with the hope of learning more. My current sense is that Watson and Strayer’s entire case for “supertaskers” hinges on research of this kind.
References
Watson, J. M., & Strayer, D. L. (2010). Supertaskers: Profiles in Extraordinary Multi-tasking Ability. Psychonomic Bulletin and Review. Forthcoming. Retrieved from http://www.psych.utah.edu/lab/appliedcognition/publications/supertaskers.pdf
Not just predicting the present, but the future: Twitter and upcoming movies
Search queries have been used recently to “predict the present“, as Hal Varian has called it. Now some initial use of Twitter chatter to predict the future:
The chatter in Twitter can accurately predict the box-office revenues of upcoming movies weeks before they are released. In fact, Tweets can predict the performance of films better than market-based predictions, such as Hollywood Stock Exchange, which have been the best predictors to date. (Kevin Kelley)
Here is the paper by Asur and Huberman from HP Labs. Also see a similar use of online discussion forums.
But the obvious question from my previous post is, how much improvement do you get by adding more inputs to the model? That is, how does the combined Hollywood Stock Exchange and Twitter chatter model perform? The authors report adding the number of theaters the movie opens in to both models, but not combining them directly.
Persuasion profiling and genres: Fogg in 2006
Maurits Kaptein and I have recently been thinking a lot about persuasion profiling — estimating and adapting to individual differences in responses to influence strategies based on past behavior and other information. With help from students, we’ve been running experiments and building statistical models that implement persuasion profiling.
My thinking on persuasion profiling is very much in BJ Fogg’s footsteps, since he has been talking about persuasion profiling in courses, lab meetings, and personal discussions since 2004 or earlier.
Just yesterday, I came across this transcript of BJ’s presentation for an FTC hearing in 2006. I was struck at how much it anticipates some of what Maurits and I have written recently (more on this later). I’m sure I watched the draft video of the presentation back then and it’s influenced me, even if I forgot some of the details.
Here is the relevant excerpt from BJ’s comments for the FTC:
Persuasion profiling means that each one of us has a different set of persuasion strategies that affect us. Just like we like different types of food or are vulnerable to giving in to different types of food on a diet, we are vulnerable to different types of persuasion strategies.
On the food example, I love old-fashioned popcorn, and if I go to a party and somebody has old-fashioned popcorn, I will probably break down and eat it. On the persuasion side of things, I know I’m vulnerable to trying new things, to challenges and to anything that gets measured. If that’s proposed to me, I’m going to be vulnerable and I’m going to give it a shot.
Whenever we go to a Web site and use an interactive system, it is likely they will be capturing what persuasion strategies work on us and will be using those when we use the service again. The mapping out of what makes me tick, what motivates me can also be bought or sold, just like a credit report.
So imagine I’m going in to buy a new car and the person selling me the car downloads my credit report but also buys my persuasion profile. I may or may not know about this. Imagine if persuasion profiles are available on political campaigns so that when I visit a Web site, the system knows it is B.J. Fogg, and it changes [its] approach based on my vulnerabilities when it comes to persuasion.
Persuasive technology will touch our lives anywhere that we access digital products or services, in the car, in our living room, on the Web, through our mobile phones and so on. Persuasive technology will be all around us, and unlike other media types, where you have 30-second commercial or a magazine ad, you have genres you can understand, when it comes to computer-based persuasion, it is so flexible that it won’t have genre boundaries. It will come to us in the ordinary course of our lives, as we are working on a Web site, as we are editing a document, as we are driving a car. There won’t be clear markers about when you are being persuaded and when you are not.
This last paragraph is about the “genrelessness” of many persuasive technologies. This isn’t directly on the topic of persuasion profiling, but I see it as critically relevant. Persuasion profiling is likely to be most effective when invisible and undisclosed to users. From this and the lack of genre-based flags for persuasive technology it follows that we will frequently be “persuasion profiled” without knowing it.