Kevin Lewis points us to this article by Michael Grosz, Julia Rohrer, and Felix Thoemmes, who write:
Causal inference is a central goal of research. However, most psychologists refrain from explicitly addressing causal research questions and avoid drawing causal inference on the basis of nonexperimental evidence. We argue that this taboo against causal inference in nonexperimental psychology impairs study design and data analysis, holds back cumulative research, leads to a disconnect between original findings and how they are interpreted in subsequent work, and limits the relevance of nonexperimental psychology for policymaking. At the same time, the taboo does not prevent researchers from interpreting findings as causal effects—the inference is simply made implicitly, and assumptions remain unarticulated. Thus, we recommend that nonexperimental psychologists begin to talk openly about causal assumptions and causal effects. Only then can researchers take advantage of recent methodological advances in causal reasoning and analysis and develop a solid understanding of the underlying causal mechanisms that can inform future research, theory, and policymakers.
What they say makes sense. But I think they are way too unskeptical about statistical methods for causal inference from nonexperimental data, what they call “recent methodological advances in causal reasoning and analysis.” See here for a more skeptical take.