The in-principle inconclusiveness of causal evidence in macroeconomics

Henschen, Tobias

doi:10.1007/s13194-018-0207-7

Cited by 5 publications

(1 citation statement)

References 18 publications

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“…In addition, one recent argument holds that confirmation of causal hypotheses in macroeconomics requires knowledge of unobservable variables, in particular of agent expectations, and is therefore necessarily infeasible (Henschen 2018). If so, and if accurate forecasting requires a verified causal model (which admittedly it might not), then macroeconomic forecasting too is necessarily infeasible.…”

Section: Third Example: Gross Domestic Productmentioning

confidence: 99%

Big data and prediction: Four case studies

Northcott

2020

Studies in History and Philosophy of Science Part A

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Has the rise of data-intensive science, or 'big data', revolutionized our ability to predict? Does it imply a new priority for prediction over causal understanding, and a diminished role for theory and human experts? I examine four important cases where prediction is desirable: political elections, the weather, GDP, and the results of interventions suggested by economic experiments. These cases suggest caution. Although big data methods are indeed very useful sometimes, in this paper's cases they improve predictions either limitedly or not at all, and their prospects of doing so in the future are limited too.

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Section: Third Example: Gross Domestic Productmentioning

confidence: 99%

Big data and prediction: Four case studies

Northcott

2020

Studies in History and Philosophy of Science Part A

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Causal Pluralism in Medicine and its Implications for Clinical Practice

Maziarz

2023

J Gen Philos Sci

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The existing philosophical views on what is the meaning of causality adequate to medicine are vastly divided. We approach this question and offer two arguments in favor of pluralism regarding concepts of causality. First, we analyze the three main types of research designs (randomized-controlled trials, observational epidemiology and laboratory research). We argue, using examples, that they allow for making causal conclusions that are best understood differently in each case (in agreement with a version of manipulationist, probabilistic and mechanistic definitions, respectively). Second, we analyze clinical practice and argue that these manipulationist, probabilistic and mechanistic causal claims can be used as evidence for different therapeutic decisions. We differentiate among ‘predicting’ that does not change the relata of causal claims, (mechanistic) ‘interferences’, and ‘interventions’ in the strict sense that act on causes to change effects. The central conclusion is that causal claims agreeing with diverse concepts of causality can deliver evidence for different types of therapeutic decisions.

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Response to Henschen: causal pluralism in macroeconomics

Maziarz

Mróz

2019

Journal of Economic Methodology

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The in-principle inconclusiveness of causal evidence in macroeconomics

Cited by 5 publications

References 18 publications

Big data and prediction: Four case studies

Big data and prediction: Four case studies

Causal Pluralism in Medicine and its Implications for Clinical Practice

Response to Henschen: causal pluralism in macroeconomics

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