Since around 2010, genome-wide association studies (GWAS) have taken the field of psychiatric genetics by storm. Many of the time-honored methods – family studies, twin analyses, adoption studies – have been largely displaced by the rush toward the identification of genetic variants that could be considered causal of psychiatric illness. At the time of this writing in 2024, the field is awash with results. For key disorders such as schizophrenia, bipolar disorder, and major depression, hundreds of variants on the genome have been found to be associated with risk of illness. What we have learned so far is that for all psychiatric disorders investigated, many common variants in the human genome appear to reflect risk, and the effect size of all of them is small, sometimes very small. Furthermore, taken individually, most of these variants do not have any obvious biological meaning; they do not occur in the parts of the genome that codes for proteins. Often, they are just statistical signals for the block of the human genome (a “haplotype”) with which they have traveled through human – and, before then, primate – evolution.

This chapter considers the concept of proximal and distal causes in various health science fields and common reference to the “proximal–distal model” of disease causation. In many cases, this model is used to capture the interaction of social and biological factors, with social factors as distal causes and biological factors as proximal causes. The authors examine Krieger’s (2008) criticism of this framework and suggestion that proximal–distal language confuses causal thinking, reasoning, and distinctions in health science domains. While Krieger (2008) has recommended the elimination of language of proximal and distal causes, Ross and Kendler argue for another alternative, specifically that while the proximal–distal model captures some etiological scenarios, it should not be applied to all cases. They examine three ways in which social and biological causes lead to health outcomes and argue for the value (and possibility) of clear notions of proximal and distal causes.

The author of the following chapter, Denny Borsboom, has played a leading role in the rise of the network model in psychopathology. This model has developed into a viable alternative to the older conventional common-factor model. These two models provide quite different explanations for why psychiatric disorders tend to cluster together in disorders. Let us illustrate this using two commonly co-occurring symptoms in individuals with major depression: insomnia and tiredness. The common-factor model assumes that the two symptoms co-occur because they are both caused by a latent construct: the psychobiological state of depression. By contrast, the network model implies a more common (and perhaps naïve) presentation, assuming a direct causal link between the two symptoms, especially in the direction of “I sleep poorly on night x” followed by “during day x + 1 I am tired.”

This chapter seeks to examine and further clarify the relationship between two main conceptual approaches to causality in psychiatric research: counterfactual and mechanistic. The author suggests that psychiatry may pose some relatively unique challenges for causal inference not shared with other branches of medicine. At their core, counterfactual approaches ask “what if” questions while mechanistic approaches ask “how” questions. The chapter also seeks to evaluate the Russo-Williamson Thesis for psychiatric research, which argues that causal inference requires evidence of counterfactual and mechanistic causal effects, by examining three research papers that examine causal effects on psychiatric disorders. Two of these are from epidemiological samples and employ counterfactual methods, and one is from molecular genetics and molecular neuroscience and utilizes a mechanistic approach. Kendler argues that these two methods are complementary and often mutually reinforcing. In particular, the demonstration of counterfactual evidence of causation naturally raises the question of how such an effect occurs at a mechanistic level. However, the author suggest that the Russo-Williamson Thesis is too high a threshold for psychiatry and that, in at least some cases, high-quality counterfactual evidence can be actionable.

I have, for many decades, read widely and deeply within the rich corpus of descriptive psychopathology. It has enriched my appreciation of the variations of the syndromes in psychiatry that we diagnose, study, and attempt to treat. It has helped me develop interview assessments used in a wide variety of psychiatric genetic studies, from family to twin, high-density pedigree, and now Genome-Wide Association Study case–control studies. I have taught two generations of psychiatric residents on the importance of this tradition and our need to know our rich descriptive inheritance. My teachings have been met with substantial (but not universal) appreciation. I have served on various DSM committees from DSM-III-R onward and tried to use my psychopathological expertise to improve our nosology, in part by increasing the empirical rigor of the evaluation for change using the concept of validators.

I recall the enthusiasm with which magnetic resonance imaging was greeted by the field of psychiatry in the 1980s, the stunning quality of the images, almost magical. This technology fed into the long desire by our field to have an innovative technology that would, in one fell swoop, solve the main problems of our field, particularly etiology and diagnosis. I recall claims such has “Now we can do neuropathology on living people,” “Now we have a tool to solve the mind–body problem,” and “It is now just a matter of time until we find the causes of schizophrenia.” A decade or more earlier it had been monoamine neurotransmitters (serotonin, norepinephrine, and dopamine) as the revolutionary scientific breakthrough, and one or two decades later it would be molecular genetics. In each case, the “hype cycle” (Dedehayir & Steinert, 2016) produced a crest of zealous excitement (and dramatic overpromising) followed by a period of disillusionment and then some stabilization and emerging maturity. The field came to recognize that the application had merits but the problems – very hard ones – of psychiatric diagnosis and etiology were not going to yield easily. Of note, the field of psychiatric neuroimaging, although now 40-plus years old, underwent another recent round of substantial criticism about the use of underpowered sample sizes (Marek et al., 2022) and inadequate corrections for multiple testing.

In a thoughtful essay that follows, Peter Zachar interweaves several philosophical issues that relate to classification in general and psychiatric classifications specifically. I think it will be most useful to interested readers if, in this introduction, I steer away from a largely conceptual/philosophical critique of Zachar’s essay and rather comment from the perspective of a working clinician and psychiatric nosologist who has, for better or worse, spend several decades working on various versions of the DSM – the accepted nosology for American psychiatry.