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The Routledge Handbook of Causality and Causal Methods
Causation and Realism
other
Author(s):
Mahdi Khalili
Publication date:
December 13 2024
Publisher:
Routledge
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There is no author summary for this book yet. Authors can add summaries to their books on ScienceOpen to make them more accessible to a non-specialist audience.
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Book Chapter
Publication date:
December 13 2024
Pages
: 379-391
DOI:
10.4324/9781003528937-43
SO-VID:
d86ec971-1880-40d7-9fb8-72288bb1c776
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Book chapters
pp. 1
Introduction
pp. 39
The Plurality of Causal Pluralisms
pp. 47
What Caused the Covid-19 Pandemic?
pp. 65
Seeing Further
pp. 75
Causal Thinking in Global Health
pp. 87
Why Adoption of Causal Modeling Methods Requires Some Metaphysics
pp. 99
The Physical Infrastructure Supporting Causal Cognition
pp. 111
Quiet Causation and Its Many Uses in Science
pp. 125
Causality in General Relativity (and Beyond)
pp. 138
Causation in Policy Science
pp. 155
The Interplay Between Single-Case and Generic Causation in Qualitative Social Science Research
pp. 167
Causation Across Levels Throughout the Sciences
pp. 178
Social Causes And Epistemic (in)Justice in Medical Machine Learning-Mediated Medical Practices
pp. 191
How Are (Causal) Systems Defined and How Are Influences From Outside Dealt With?
pp. 202
Individuation of Cross-Cutting Causal Systems in Cognitive Science and Behavioral Ecology
pp. 215
Closure of Constraints and the Individuation of Causal Systems in Biology
pp. 231
The Challenge of Complexity
pp. 241
A Pluralistic (Mosaic) Approach to Causality in Health Complexity
pp. 255
Causal Cycles in Biology
pp. 269
Modelling Cyclic Causal Structures
pp. 283
Physical vs Biomedical Sciences
pp. 300
Non-Experimental Interventions in Political Science and International Relations
pp. 311
Information Security, Intelligence Analysis, and Knowledge Generation Without Experiments
pp. 325
Causality Problems in Machine Learning Systems
pp. 342
Technology-Driven Causal Inference
pp. 353
The Combination of Brain Stimulation and Brain Imaging Technologies in the Cognitive Neurosciences
pp. 364
Causal-Manipulationist Approaches to Explaining Machine Learning
pp. 379
Causation and Realism
pp. 392
Using Deep Neural Networks and Similarity Metrics to Predict and Control Brain Responses
pp. 407
Between Quantity and Quality
pp. 419
Process Tracing with Qualitative Data
pp. 433
Moving Beyond Explanatory Monism
pp. 444
Comparing Prediction and Explanation in Computational Models
pp. 454
Causal Mechanisms in the Social Sciences as Evidence for Higher-Order Causal Relations
pp. 467
When Does an Event Become a Cause?
pp. 479
Heterogeneous Causality
pp. 493
When Decisions Must Be Based on Partial Causal Knowledge
pp. 506
Going From Models to Action
pp. 519
Evidence, Causation, Guidelines and Regulation
pp. 532
Causal Evidence and the Social Determinants of Health
pp. 543
Causation, Regulation, and the Assessment of Adverse Events Following Immunization (AEFIs)
pp. 556
Science to Policy Through Adverse Outcome Pathways1
pp. 571
Causal Knowledge and the Process Of Policy Making
pp. 588
From Evidence to Policy
pp. 601
Causality and Interdisciplinarity in the Philosophy of Science in Practice
pp. 614
What to Do When You Encounter Funky Causes in the (Historical) Wild
pp. 625
Clinical Reasoning as a Problem-Solving Cognitive Activity
pp. 637
Practical Causal Knowledge for Sustainability
pp. 652
Causality and Complex Systems in the Geosciences
pp. 661
Causal Thinking in Science Education and the Challenges It Holds
pp. 671
Causal Reasoning About Education
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