The Routledge Handbook of Causality and Causal Methods

Cover
Half Title
Series Page
Title Page
Copyright Page
Contents
Notes on Contributors
Introduction: The Mosaic of Causal Theory: Whence and Whither
PART I: Causal Pluralism from Theory to Practice
	1. The Plurality of Causal Pluralisms
	2. What Caused the COVID-19. Pandemic?
PART II: Causal Theory and the Role of Researchers
What is the variety of roles of the researchers (or groups of researchers) in the practices of causal discovery and validation?
	3. Seeing Further: The Role of Modelers and Simulation in Causal Inference
	4. Causal Thinking in Global Health: Pragmatism and the Causal Mosaic
How is causality fundamental and/or practical in different disciplines?
	5. Why Adoption of Causal Modeling Methods Requires Some Metaphysics
	6. The Physical Infrastructure Supporting Causal Cognition: Locality and Asymmetry
	7. Quiet Causation and Its Many Uses in Science
When are deeper ontological assumptions important and when are they not?
	8. Causality in General Relativity (and Beyond): Heuristics from Metaphysics
	9. Causation in Policy Science: Knowledge, Power, Meaning, Agency and Context
PART III: Features of Causal Systems
Are there levels of causation? If so, what are they?
	10. The Interplay between Single-Case and Generic Causation in Qualitative Social Science Research
	11. Causation Across Levels throughout the Sciences
	12. Social Causes and Epistemic (In)justice in Medical Machine Learning-Mediated Medical Practices
What are the boundaries of (causal) systems? How should we establish or cope with them?
	13. How Are (Causal) Systems Defined and How Are Influences from Outside Dealt With?
	14. Individuation of Cross-Cutting Causal Systems in Cognitive Science and Behavioral Ecology
	15. Closure of Constraints and the Individuation of Causal Systems in Biology
What aspects of causal complexity are important and how are they handled in research?
	16. The Challenge of Complexity: Causal Inference and Simulation Models in Macroeconomics
	17. A Pluralistic (Mosaic) Approach to Causality in Health Complexity
What are the challenges of causal cycles, and what are the best ways of meeting them?
	18. Causal Cycles in Biology
	19. Modelling Cyclic Causal Structures
PART IV: Causal Methods, Experimentation and Observation
Under what circumstances is it (not) necessary to intervene experimentally? Or even to use non-experimental methods?
	20. Physical vs Biomedical Sciences: Only the Latter Needs RCTs, but Both Require Careful and Honest Methodology
	21. Non-Experimental Interventions in Political Science and International Relations
	22. Information Security, Intelligence Analysis, and Knowledge Generation Without Experiments
How is technology advancing or hindering causal reasoning? Or allowing increased epistemic access to causal relations?
	23. Causality Problems in Machine Learning Systems
	24. Technology-Driven Causal Inference: Prospects and Challenges
	25. The Combination of Brain Stimulation and Brain Imaging Technologies in the Cognitive Neurosciences: Problematizing the “Convergence Hypothesis”
	26. Causal-Manipulationist Approaches to Explaining Machine Learning
PART V: Measurement and Data
What kind of metrics or measurement methods do causal methods need?
	27. Causation and Realism: The Role of Instrumentally Mediated Empirical Evidence
	28. Using Deep Neural Networks and Similarity Metrics to Predict and Control Brain Responses
What is ‘good quality’ data for causal inference?
	29. Between Quantity and Quality: Competing Views on the Role of Big Data for Causal Inference
	30. Process Tracing with Qualitative Data
PART VI: Causality, Knowledge, and Action
What are the practices of causal explanation?
	31. Moving Beyond Explanatory Monism
	32. Comparing Prediction and Explanation in Computational Models: Theoretical Neuroscience versus Language Technology
	33. Causal Mechanisms in the Social Sciences as Evidence for Higher-Order Causal Relations
	34. When Does an Event Become a Cause?: Narrative Structure and Causal Indeterminacy
	35. Heterogeneous Causality: Levels of Causation and the WHOW Causal Logics in Qualitative Comparative Analysis
Do we need full knowledge of a system in order to establish causes? What can be done with partial knowledge?
	36. When Decisions Must be Based on Partial Causal Knowledge: Analyzing Causality and Evidence for Health Policy
	37. Going from Models to Action: Using Causal Knowledge for Everyday Choices
How is causal evidence to be used in regulatory contexts?
	38. Evidence, Causation, Guidelines and Regulation: The Public Health Experience of NICE in England
	39. Causal Evidence and the Social Determinants of Health: The Case of the Adverse Childhood Experiences Policies
	40. Causation, Regulation, and the Assessment of Adverse Events Following Immunization (AEFIs)
	41. Science to Policy through Adverse Outcome Pathways
	42. Causal Knowledge and the Process of Policy Making: Toward a Bottom-Up Approach
	43. From Evidence to Policy: Assessing Causal Claims in Nutrition Science
PART VII: Causal Theory Across Disciplinary Borders
How to theorize causality outside the canon?
	44. Causality and Interdisciplinarity in the Philosophy of Science in Practice: The Cases of Ecology and Environmental Conservation
	45. What to Do When You Encounter Funky Causes in the (Historical) Wild
Where should we pioneer causal theory outside philosophical canon?
	46. Clinical Reasoning as a Problem-Solving Cognitive Activity: The Role of Causal Claims
	47. Practical Causal Knowledge for Sustainability: Implications of Co-Production for a Philosophical Understanding of Causality in Sustainability Science
	48. Causality and Complex Systems in the Geosciences
How does causal theory make it into the classroom?
	49. Causal Thinking in Science Education and the Challenges it Holds
	50. Causal Reasoning About Education: What Is It and What Should It Be?
Index