Hermeneutic single-case efficacy design

In this article, I outline hermeneutic single-case efficacy design (HSCED), an interpretive approach to evaluating treatment causality in single therapy cases. This approach uses a mixture of quantitative and qualitative methods to create a network of evidence that first identifies direct demonstrations of causal links between therapy process and outcome and then evaluates plausible nontherapy explanations for apparent change in therapy. I illustrate the method with data from a depressed client who presented with unresolved loss and anger issues

Method for Enabling Causal Inference in Relational Domains

The analysis of data from complex systems is quickly becoming a fundamental aspect of modern business, government, and science. The field of causal learning is concerned with developing a set of statistical methods that allow practitioners make inferences about unseen interventions. This field has seen significant advances in recent years. However, the vast majority of this work assumes that data instances are independent, whereas many systems are best described in terms of interconnected instances, i.e. relational systems. This discrepancy prevents causal inference techniques from being reliably applied in many real-world settings. In this thesis, I will present three contributions to the field of causal inference that seek to enable the analysis of relational systems. First, I will present theory for consistently testing statistical dependence in relational domains. I then show how the significance of this test can be measured in practice using a novel bootstrap method for structured domains. Second, I show that statistical dependence in relational domains is inherently asymmetric, implying a simple test of causal direction from observational data. This test requires no assumptions on either the marginal distributions of variables or the functional form of dependence. Third, I describe relational causal adjustment, a procedure to identify the effects of arbitrary interventions from observational relational data via an extension of Pearl\u27s backdoor criterion. A series of evaluations on synthetic domains shows the estimates obtained by relational causal adjustment are close to those obtained from explicit experimentation

The science of psychoanalysis

For psychoanalysis to qualify as scientific psychology, it needs to generate data that can evidentially support theoretical claims. Its methods, therefore, must at least be capable of correcting for biases produced in the data during the process of generating it; and we must be able to use the data in sound forms of inference and reasoning. Critics of psychoanalysis have claimed that it fails on both counts, and thus whatever warrant its claims have derive from other sources. In this article, I discuss three key objections, and then consider their implications together with recent developments in the generation and testing of psychoanalytic theory. The first and most famous is that of ‘suggestion’; if it sticks, clinical data may be biased in a way that renders all inferences from them unreliable. The second, sometimes confused with the first, questions whether the data are or can be used to provide genuine tests of theoretical hypotheses. The third will require us to consider the question of how psychology can reliably infer motives from behavior. I argue that the clinical method of psychoanalysis is defensible against these objections in relation to the psychodynamic model of mind, but not wider metapsychological and etiological claims. Nevertheless, the claim of psychoanalysis to be a science would be strengthened if awareness of the methodological pitfalls and means to avoid them, and alternative theories and their evidence bases, were more widespread. This may require changes in the education of psychoanalysts

Reasoning about Independence in Probabilistic Models of Relational Data

We extend the theory of d-separation to cases in which data instances are not independent and identically distributed. We show that applying the rules of d-separation directly to the structure of probabilistic models of relational data inaccurately infers conditional independence. We introduce relational d-separation, a theory for deriving conditional independence facts from relational models. We provide a new representation, the abstract ground graph, that enables a sound, complete, and computationally efficient method for answering d-separation queries about relational models, and we present empirical results that demonstrate effectiveness.Comment: 61 pages, substantial revisions to formalisms, theory, and related wor

Russell on Introspection and Self-Knowledge

This chapter examines Bertrand Russell's developing views--roughly from 1911 to 1918--on the nature of introspective knowledge and subjects' most basic knowledge of themselves as themselves. It argues that Russell's theory of introspection distinguishes between direct awareness of individual psychological objects and features, the presentation of psychological complexes involving those objects and features, and introspective judgments which aim to correspond with them. It also explores his transition from believing that subjects enjoy introspective self-acquaintance, to believing that they only know themselves by self-description, and eventually to believing that self-knowledge is a logical construction. It concludes by sketching how Russell's views about introspection and self-knowledge change as a result of his adoption of neutral monism. Along the way, it sheds additional light on his acquaintance-based theory of knowledge, preference for logical constructions over inferred entities, and gradual progression towards neutral monism

Evidence for Information Processing in the Brain

Many cognitive and neuroscientists attempt to assign biological functions to brain structures. To achieve this end, scientists perform experiments that relate the physical properties of brain structures to organism-level abilities, behaviors, and environmental stimuli. Researchers make use of various measuring instruments and methodological techniques to obtain this kind of relational evidence, ranging from single-unit electrophysiology and optogenetics to whole brain functional MRI. Each experiment is intended to identify brain function. However, seemingly independent of experimental evidence, many cognitive scientists, neuroscientists, and philosophers of science assume that the brain processes information as a scientific fact. In this work we analyze categories of relational evidence and find that although physical features of specific brain areas selectively covary with external stimuli and abilities, and that the brain shows reliable causal organization, there is no direct evidence supporting the claim that information processing is a natural function of the brain. We conclude that the belief in brain information processing adds little to the science of cognitive science and functions primarily as a metaphor for efficient communication of neuroscientific data