Causal Inference and Causal Explanation

1982 ◽  
pp. 179-191 ◽  
Author(s):  
Clark Glymour
Keyword(s):  
Causal Inference ◽  
Causal Explanation

scholarly journals Subjective Causality and Counterfactuals in the Social Sciences: Toward an Ethnographic Causality?

2019 ◽  
pp. 004912411985237
Author(s):  
Peter Abell ◽  
Ofer Engel
Keyword(s):  
Social Sciences ◽  
Causal Inference ◽  
Statistical Inference ◽  
Observational Studies ◽  
Causal Explanation ◽  
Statistical Concept ◽  
Ethnographic Studies ◽  
The Social ◽  
Subjective Evidence ◽  
Subsequent Comparison

The article explores the role that subjective evidence of causality and associated counterfactuals and counterpotentials might play in the social sciences where comparative cases are scarce. This scarcity rules out statistical inference based upon frequencies and usually invites in-depth ethnographic studies. Thus, if causality is to be preserved in such situations, a conception of ethnographic causal inference is required. Ethnographic causality inverts the standard statistical concept of causal explanation in observational studies, whereby comparison and generalization, across a sample of cases, are both necessary prerequisites for any causal inference. Ethnographic causality allows, in contrast, for causal explanation prior to any subsequent comparison or generalization.

scholarly journals Causality in life course research: the potential use of ‘natural experiments’ for causal inference

2020 ◽  
Vol 11 (1) ◽  
pp. 7-25
Author(s):  
Ross Macmillan ◽  
Carmel Hannan
Keyword(s):  
Life Course ◽  
Causal Inference ◽  
Critical Realism ◽  
Causal Explanation ◽  
Theoretical Development ◽  
Natural Experiments ◽  
Statistical Estimates ◽  
Road Map ◽  
Theoretical Perspectives ◽  
The Life Course

Recent decades have seen renewed attention to issues of causal inference in the social sciences, yet implications for life course research have not been spelled out nor is it clear what types of approaches are best suited for theoretical development on life course processes. We begin by evaluating a number of meta-theoretical perspectives, including critical realism, data mining and experimentation, and find them limited in their potential for causal claims in a life course context. From this, we initiate a discussion of the logic and practice of ‘natural experiments’ for life course research, highlighting issues of how to identify natural experiments, how to use cohort information and variation in the order and timing of life course transitions to isolate variation in exposure, how such events that alter social structures are the key to identification in causal processes of the life course and, finally, of analytic strategies for the extraction of causal conclusions from conventional statistical estimates. Through discussion of both positive and negative examples, we outline the key methodological issues in play and provide a road map of best practices. While we acknowledge that causal claims are not necessary for social explanation, our goal is to explain how causal inference can benefit life course scholarship and outline a set of practices that can complement conventional approaches in the pursuit of causal explanation in life course research.

Causal Explanation

2017 ◽  
Author(s):  
Tania Lombrozo ◽  
Nadya Vasilyeva
Keyword(s):  
Causal Inference ◽  
Causal Reasoning ◽  
Causal Explanation ◽  
Future Research ◽  
Causal Mechanisms ◽  
The Relationship

Explanation and causation are intimately related. Explanations often appeal to causes, and causal claims are often answers to implicit or explicit questions about why or how something occurred. This chapter considers what we can learn about causal reasoning from research on explanation. In particular, it reviews an emerging body of work suggesting that explanatory considerations—such as the simplicity or scope of a causal hypothesis—can systematically influence causal inference and learning. It also discusses proposed distinctions among types of explanations and reviews the effects of each explanation type on causal reasoning and representation. Finally, it considers the relationship between explanations and causal mechanisms and raises important questions for future research.

Causal Models in Psychiatric Research

1986 ◽  
Vol 148 (6) ◽  
pp. 713-717 ◽  
Author(s):  
Matthew J. Edlund
Keyword(s):  
Causal Inference ◽  
Causal Explanation ◽  
Causal Models ◽  
Psychiatric Research ◽  
Conceptual Difficulty

The nature of causal explanation remains a conceptual difficulty for psychiatric research. Most research workers and clinicians can describe their processes of causal inference with reference to some empirical example close at hand. Yet such demonstrations are themselves based on more fundamental abstract models that are themselves left unstated. Psychiatric classificatory schemata presently used in research possess unwritten, underlying assumptions regarding causality, and these assumptions need examination.

Beyond metaphors and semantics: A framework for causal inference in neuroscience

2019 ◽  
Vol 42 ◽  
Author(s):  
Roberto A. Gulli
Keyword(s):  
Causal Inference ◽  
Causal Power

Abstract The long-enduring coding metaphor is deemed problematic because it imbues correlational evidence with causal power. In neuroscience, most research is correlational or conditionally correlational; this research, in aggregate, informs causal inference. Rather than prescribing semantics used in correlational studies, it would be useful for neuroscientists to focus on a constructive syntax to guide principled causal inference.

A Causal Inference Explanation for Multisensory Speech Perception

2013 ◽  
Author(s):  
John F. Magnotti ◽  
Wei Ji Ma ◽  
Michael S. Beauchamp
Keyword(s):  
Speech Perception ◽  
Causal Inference
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