One of the most misguided and dangerous ideas floated around by a group of Big Data enthusiasts is the notion that it is not important to understand why something happens, just because "we have a boatload of data". This is one of the central arguments in the bestseller Big Data, and it reached the mainstream much earlier when Chris Anderson, then chief editor of Wired, published his flamboyantly-titled op-ed proclaiming the "End of Theory."
In making the claim that causal analysis is hopeless and pointless, these proponents are disowning entire fields of study. While focusing their vitrol on the social sciences, they somehow miss the obvious: that causal thinking is and has always been the foundation of the physical sciences and engineering. Even business executives understand the primacy of "root-cause analysis."
Contrary to these folks, I believe social scientists will produce the most exciting research on causal analysis. Human behavior is generally much more variable than natural phenomena, complicating the search for causes. We need even smarter people to tackle these problems.
That said, a lot of published social science exhibit flawed thinking about causes. Andrew and I describe a few of the problems in the latest Statbusters (link). An assumption is often made that an observed effect is due to a single cause. Much effort is expensed on identifying this one cause from a slate of candidates. Further, it is just as important for us to know which studies failed but such failure is never reported in journals or the media. This publication bias results in researchers examining the same correlations over and over again, and eventually one research group will discover a "statistically significant" effect and get it published, even though in reality, the totality of the evidence would contradict the one published study.