The Hubble Hypothesis and the Developmentalist's Dilemma

Developmental psychopathology stands poised at the close of the 20th century on the horns of a major scientific dilemma. The essence of this dilemma lies in the contrast between its heuristically rich open system concepts on the one hand, and the closed system paradigm it adopted from mainstream psychology for investigating those models on the other. Many of the research methods, assessment strategies, and data analytic models of psychology’s paradigm are predicated on closed system assumptions and explanatory models. Thus, they are fundamentally inadequate forstudying humans, who are unparalleled among open systems in their wide ranging capacities for equifinal and multifinal functioning. Developmental psychopathology faces two challenges in successfully negotiating the developmentalist’s dilemma. The first lies in recognizing how the current paradigm encourages research practices that are antithetical to developmental principles, yet continue to flourish. I argue that the developmentalist’s dilemma is sustained by long standing, mutually enabling weaknesses in the paradigm’s discovery methods and scientific standards. These interdependent weaknesses function like a distorted lens on the research process by variously sustaining the illusion of theoretical progress, obscuring the need for fundamental reforms, and both constraining and misguiding reform efforts. An understanding of how these influences arise and take their toll provides a foundation and rationale for engaging the second challenge. The essence of this challenge will be finding ways to resolve the developmentalist’s dilemma outside the constraints of the existing paradigm by developing indigenous research strategies, methods, and standards with fidelity to the complexity of developmental phenomena

The role of assumptions in causal discovery

The paper looks at the conditional independence search approach to causal discovery, proposed by Spirtes et al. and Pearl and Verma, from the point of view of the mechanism-based view of causality in econometrics, explicated by Simon. As demonstrated by Simon, the problem of determining the causal structure from data is severely underconstrained and the perceived causal structure depends on the a priori assumptions that one is willing to make. I discuss the assumptions made in the independence search-based causal discovery and their identifying strength

Experimental effects and causal representations

In experimental settings, scientists often “make” new things, in which case the aim is to intervene in order to produce experimental objects and processes—characterized as ‘effects’. In this discussion, I illuminate an important performative function in measurement and experimentation in general: intervention-based experimental production (IEP). I argue that even though the goal of IEP is the production of new effects, it can be informative for causal details in scientific representations. Specifically, IEP can be informative about causal relations in: regularities under study; ‘intervention systems’, which are measurement/experimental systems; and new technological systems