Causal Scoring: A Framework for Effect Estimation, Effect Ordering, and
Effect Classification
- URL: http://arxiv.org/abs/2206.12532v4
- Date: Fri, 16 Feb 2024 12:05:26 GMT
- Title: Causal Scoring: A Framework for Effect Estimation, Effect Ordering, and
Effect Classification
- Authors: Carlos Fern\'andez-Lor\'ia and Jorge Lor\'ia
- Abstract summary: Causal scoring entails the estimation of scores that support decision making by providing insights into causal effects.
We present three valuable causal interpretations of these scores: effect estimation (EE), effect ordering (EO), and effect classification (EC)
- Score: 11.460911023224337
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This paper introduces causal scoring as a novel approach to frame causal
estimation in the context of decision making. Causal scoring entails the
estimation of scores that support decision making by providing insights into
causal effects. We present three valuable causal interpretations of these
scores: effect estimation (EE), effect ordering (EO), and effect classification
(EC). In the EE interpretation, the causal score represents the effect itself.
The EO interpretation implies that the score can serve as a proxy for the
magnitude of the effect, enabling the sorting of individuals based on their
causal effects. The EC interpretation enables the classification of individuals
into high- and low-effect categories using a predefined threshold. We
demonstrate the value of these alternative causal interpretations (EO and EC)
through two key results. First, we show that aligning the statistical modeling
with the desired causal interpretation improves the accuracy of causal
estimation. Second, we establish that more flexible causal interpretations are
plausible in a wider range of settings and propose conditions to assess their
validity. We showcase the practical utility of causal scoring through diverse
scenarios, including situations involving unobserved confounding due to
self-selection, lack of data on the primary outcome of interest, or lack of
data on how individuals behave when intervened. These examples illustrate how
causal scoring facilitates reasoning about flexible causal interpretations of
statistical estimates in various contexts. They encompass confounded estimates,
effect estimates on surrogate outcomes, and even predictions about non-causal
quantities as potential causal scores.
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