Topic: causal inference

How reliably can non-invasive brain stimulation establish causal relationships in cognition, and which factors change its effects? This workshop examines the empirical, methodological and theoretical limits of causal inference with transcranial stimulation. It connects stimulation protocols, individual variability and biomarkers with multi-laboratory replication and open-science practice. The programme covers concurrent TMS-fMRI, white-matter pathways, paired associative stimulation, electric-field modelling and memory-related electrical stimulation. A satellite session includes practical TMS-

The ALGOSH and DIGILONG projects study how algorithmic management and digital platform work affect working conditions and health. The researcher will combine longitudinal surveys with Swedish population registers, develop questions and analytical strategies, lead publications and collaborate with PhD students. Methods may include longitudinal causal inference, target trial emulation, mediation, machine learning and complexity-informed approaches, chosen to answer substantive occupational-health questions. The full-time, two-year position is based at the Unit of Occupational Medicine in Solna.

Harvard T.H. Chan School of Public Health Department of Epidemiology seminar by Jeremy Labrecque, PhD, MSc (Assistant Professor of Epidemiology and Causal Inference, Erasmus MC, Rotterdam) on 'dark causal inference' — implied but unstated causal reasoning that pervades epidemiology — the misuse of 'association' language, and gaps in teaching causal reasoning.

Cognitive neuroscience has witnessed great progress since modern neuroimaging embraced an open science framework, with the adoption of shared principles (Wilkinson et al., 2016), standards (Gorgolewski et al., 2016), and ontologies (Poldrack et al., 2011), as well as practices of meta-analysis (Yarkoni et al., 2011; Dockès et al., 2020) and data sharing (Gorgolewski et al., 2015). However, while functional neuroimaging data provide correlational maps between cognitive functions and activated brain regions, its usefulness in determining causal link between specific brain regions and given behav

A major challenge for causal inference from time-series data is the trade-off between computational feasibility and accuracy. Motivated by process motifs for lagged covariance in an autoregressive model with slow mean-reversion, we propose to infer networks of causal relations via pairwise edge measure (PEMs) that one can easily compute from lagged correlation matrices. Motivated by contributions of process motifs to covariance and lagged variance, we formulate two PEMs that correct for confounding factors and for reverse causation. To demonstrate the performance of our PEMs, we consider netwo

Natural perception relies inherently on inferring causal structure in the environment. However, the neural mechanisms and functional circuits that are essential for representing and updating the hidden causal structure during multisensory processing are unknown. To address this, monkeys were trained to infer the probability of a potential common source from visual and proprioceptive signals on the basis of their spatial disparity in a virtual reality system. The proprioceptive drift reported by monkeys demonstrated that they combined historical information and current multisensory signals to e

Our senses are constantly bombarded with a myriad of diverse signals. Transforming this sensory cacophony into a coherent percept of our environment relies on solving two computational challenges: First, we need to solve the causal inference problem - deciding whether signals come from a common cause and thus should be integrated, or come from different sources and be treated independently. Second, when there is a common cause, we should integrate signals across the senses weighted in proportion to their sensory reliabilities. I discuss recent research at the behavioural, computational and neu

Motor and premotor cortical activities reflect motor planning and execution. For stereotyped movements, the internal cortical state should be predictive of subsequent motor commands. We developed a statistical method to infer a nonlinear state-space model that extracts an effective systems-level description of neural dynamics from population recordings. Without the knowledge of the task, target, or motor behavior, the inferred model can forecast neural activity at a single-trial level. Our novel inference algorithm performs approximate Bayesian filtering and smoothing within a structured varia