Causality, politics, and the net

Henry Farrell recently declared himself against studying the internet, and while that headline oversells his argument a bit, compelling turns of phrase are a large part of what gets good online conversations started. His basic thesis is that we should not only not study “the internet” as a system isolated from the rest of society, but also that we should trade analyses of specific online platforms (Facebook, Twitter, Youtube, etc.) for analyses of abstract causal mechanisms—some of which may flourish upon those platforms, but which are almost certainly not limited to them—that contribute to various sociopolitical outcomes. This perspective is more or less a direct application of one of the most fundamental normative stances of mainstream political science (among other branches of social science), namely that of causality as the gold standard of social research. (This position is not universal, as the existence of antipositivism attests.) I agree with Henry that causality is wonderful if you can demonstrate it, but think we need to get a bit more specific about exactly what we’re talking about before we venture too far.

Explaining my reservations will require shedding a bit of light on three interrelated questions. The first of these is: what do we mean by “causality” in this context? Secondly, what factors can and cannot be causes of political outcomes? Finally, what are the prospects of causal analysis of ICT-augmented politics?

The term “causality” carries multiple definitions in different contexts. For the purposes of this blog post, I intend the nomothetic and probabilistic sense of the term that is used widely throughout the social sciences. “Nomothetic” simply means covering a wide variety of cases, as opposed to “idiographic” causes which only apply to a single case. Babbie (2008) lists three widely-used criteria for nomothetic causality: correlation, time precedence, and nonspuriousness. (Alternative criteria for nomothetic causality are offered by Brady [2008] and Rubin [1980].) Correlation and time precedence should be self-explanatory for anyone with a passing familiarity with social science, and nonspuriousness simply means having eliminated most major alternative explanations and potential hidden variables. Probabilism refers to causes that increase the likelihood of a given outcome rather than guaranteeing it. Probabilistic causes are neither necessary nor sufficient, but their effects are robust enough for them to serve as meaningful predictors of the social outcome(s) in question.

It is difficult to see how ICTs by themselves could serve as causes of any given political outcome in this sense. Correlation is difficult to demonstrate because technologies are often associated with wildly divergent social outcomes in different social contexts (Markus & Robey, 1988). This is the main reason why there are very few scholarly technological determinists working today. Time precedence is also hard to straighten out in societies suffused with ICTs and proficient users. The question of which came first—political action or ICT use—will increasingly yield a single, unenlightening answer: the latter, as more and more people begin using digital technologies at early ages. Nonspuriousness presents probably the strongest objection of the three, as net skeptics have marshaled various alternative explanations for ostensibly net-driven political participation (e.g. Hindman, 2008, Margolis & Resnick, 2000).

But this point doesn’t really dent Henry’s argument at all, because he doesn’t posit technology as a cause. Rather, he focuses on social processes such as peer-to-peer information sharing and social influence as potential causes of political phenomena. It seems clear that these variables could in principle function as causes, but if they’re doing all the work, what do we need the internet for? One possibility is that online access or the use of specific services are effects rather than causes: this is the position of the normalization hypothesis, which holds that preexisting political interests cause political uses of ICTs. Another is that the role of technology is simply too complex to theorize as nomothetically as we might like, as Markus and Robey’s empirical review suggests.

In any event, the nomothetic approach requires that the social processes of interest retain their predictive power across a wide array of cases. Thus it is not enough that social influence, for example, might be linked with revolutionary activities in a few countries or situations—the two would need to be correlated, properly time-sequenced, and spuriousness-tested in many if not most cases to support a strong general theory of political action. Failing this rather lofty empirical standard, we might profitably settle for devising theories of smaller subsets of cases that are conceptually linked in some way. So it might be possible to develop theories of (for example) protest activity in advanced democracies, developing countries, or Islamic countries that include distinctive sets of roles for ICTs (e.g. Howard, 2010). But notice how far we have moved from macro-level theories that posit context-independent relationships between social processes and politics, whose breadth makes them unlikely to accrue consistent empirical support. By bounding our theoretical scopes with contextual qualifiers like culture, country, time period, and level of technological development, it is possible to develop mid-level theories that strike a balance between explaining It All and simply describing reality.

In sum, all of this is to say: yes to mechanisms, but yes also to both tightly circumscribed contextual caveats and the possibility of significant roles for online platforms. In the end I think my position is compatible with a version of Henry’s; my primary concerns are about the scope of generalizability of causal claims. Big, broad, parsimonious theory is always attractive, but it may not always be possible.

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