As far as I can see, a lot of the astronomical number of citations Paul Holland's article "Statistics and Causal Inference" has accumulated is due to the fact that he coined the term "fundamental problem of causality", describing the fact that you can observe a unit only with or without treatment, but not both. Every time you mention that problem, you're pretty much obliged to cite Holland. I'm not so happy with Holland's coinage of the term, because I would have thought that an even more fundamental problem of causality - or rather, the estimation of causal influences - than the one Holland referred to is that you cannot observe causality.
Like many others, I am also unhappy with the maxim "No causation without manipulation" that Holland's article spread. Writes Markus Gangl in his pretty good (but not untechnical) review of causal inference with observational data (pp. 38-39; gated link):
The perception that the counterfactual framework would primarily apply to the effects of policy interventions or other explicitly manipulated (or at least manipulable) treatments is perhaps the single most important impediment to its more widespread adoption in sociology. This perception is a major misunderstanding on the part of sociologists (cf. also Heckman 2005, Moffitt 2005, Sobel 1998). Whether nonmanipulable factors such as gender, race, or class affect life courses is a perfectly sensible counterfactual question to begin with [...]. With respect to gender, for example, the counterfactual “manipulation” in question is the determination of fetal gender at inception, which, moreover, is plausibly random (Rubin 1986), so that its causal effect is directly identified from the comparison of mean life-course outcomes among men and women from, for example, the same birth cohort or country. In this specific case, and ignoring SUTVA [...], the main impediment to causal inference is not so much a lack of controls, as a lack of representative samples (see Sobel 1998).
That's right: If you want to look at the causal effects of gender, just compare group means. I don't know, though, what Gangl is getting at with his remark on representative data - there's quite a few representative datasets that contain both men and women. The problem (that Gangl hints at in technical, general terms on p. 39) is rather that nobody really cares about the total effect of assignment to a sex. What people care about are the mediating mechanisms - testosterone, discrimination, that kind of thing.
Speaking of discrimination, feminism is a bewilderingly imprecise term, but I have found the following, very reductionist, model helpful when thinking about the strand sometimes called "radical feminism." You can see it as an ideology that took two figures of thought referring to the interrelationship between groups and applying them to gender. From anti-racism, radical feminism took the idea that differences between groups cannot be based on biological differences. From Marxism came the idea that history is mainly the struggle between groups, which leads to fanciful statements such as the description of rape as "nothing more or less than a conscious process of intimidation by which all men keep all women in a state of fear."
Note that the two combine nicely to shut off uncertainty. The no-biology view tells us that all observed differences between men and women must be due to discrimination of one sort or another; the men-against-women view tells us who's doing the discriminating. No further research needed.