7 citations · 17 across the 21 of their papers we have counts for
6 papers · 1 filter
Typical Yet Unlikely and Normally Abnormal: The Intuition Behind High-Dimensional Statistics
Matthew J. Vowels
Normality, in the colloquial sense, has historically been considered an aspirational trait, synonymous with ideality. The arithmetic average and, by extension, statistics including…
Causal Effect Identification in Uncertain Causal Networks
Sina Akbari, Fateme Jamshidi, Ehsan Mokhtarian +3
Causal identification is at the core of the causal inference literature, where complete algorithms have been proposed to identify causal queries of interest. The validity of these…
A Causal Research Pipeline and Tutorial for Psychologists and Social Scientists
Matthew J. Vowels
Causality is a fundamental part of the scientific endeavour to understand the world. Unfortunately, causality is still taboo in much of psychology and social science. Motivated by…
Prespecification of Structure for Optimizing Data Collection and Research Transparency by Leveraging Conditional Independencies
Matthew J. Vowels
Data collection and research methodology represents a critical part of the research pipeline. On the one hand, it is important that we collect data in a way that maximises the vali…
Trying to Outrun Causality with Machine Learning: Limitations of Model Explainability Techniques for Identifying Predictive Variables
Matthew J. Vowels
Machine Learning explainability techniques have been proposed as a means of `explaining' or interrogating a model in order to understand why a particular decision or prediction has…
A Free Lunch with Influence Functions? Improving Neural Network Estimates with Concepts from Semiparametric Statistics
Matthew J. Vowels, Sina Akbari, Necati Cihan Camgoz +1
Parameter estimation in empirical fields is usually undertaken using parametric models, and such models readily facilitate statistical inference. Unfortunately, they are unlikely t…