As is taught in beginning research methods courses, the only technique that allows for causal inference is the true experiment and the linchpin of experimentation that permits such inference is random assignment. In the social and behavioral sciences, experiments typically are conducted in university laboratories, with established subject pools to ensure the availability of participants.
Scientists lacking such resources may thus have a hard time conducting experiments, even if they wanted to. Others may simply develop a preference for survey research or other non-experimental methods such as archival research and content analysis; such techniques generally cannot be used to assess causality, but offer potential advantages in terms of mapping onto more natural, realistic situations encountered in daily life. I myself, for whatever reason, gravitated to survey research over the years, even though laboratory experimentation was a major part of my graduate-school experience.
Now, however, even scholars lacking an affinity for the lab may have opportunities to address causation in their research. In what appears to be a growing trend, clever researchers are noticing random assignments in real-world settings and seizing upon them to conduct causal studies from afar.
As one example, University of Durham anthropologists Russell Hill and Robert Barton realized that in Olympic "combat" sports such as boxing and wrestling, competitors are assigned at random to wear either red or blue outfits. The finding that red-clad participants won more often than did their blue counterparts can thus be interpreted causally (a showing that outfit color has some causal effect does not necessarily mean that it's a large effect).
As another example, readers of the 2005 book Freakonomics (by Steven Levitt and Stephen Dubner) may remember Levitt and colleagues' drawing upon the Chicago Public Schools' use of random assignment in the district's school-choice program when more students wanted to go to a particular school than could be accommodated. As Levitt and Dubner wrote (p. 158):
In the interest of fairness [to applicants of the most competitive schools], the CPS resorted to a lottery. For a researcher, this is a remarkable boon. A behavioral scientist could hardly design a better experiment in his laboratory...
The random aspect thus allowed the researchers to make the causal conclusion that:
...the students who won the lottery and went to a "better" school did no better than equivalent students who lost the lottery and were left behind.
In yet another example, Levitt's fellow University of Chicago faculty member, law professor Cass Sunstein, seized upon the fact that appellate cases within the federal circuit courts are heard by random three-judge panels from the larger pool of judges within each geographic circuit. Does a judge appointed by a Democratic (or Republican) president show variation in how often he or she votes on the bench in a liberal or conservative direction, depending on whether he or she is joined in a case by judges appointed by presidents of the same or opposing party? That is the type of question Sunstein and his colleagues can answer (here and here).
Yale law professor (and econometrician) Ian Ayres, whose 2007 book Supercrunchers I recently finished reading, refers to this phenomenon as "piggyback[ing] on pre-existing randomization" (p. 72). The book contains additional examples of its use.