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Statistical Models and Causal Inference by Jasjeet S. Sekhon, David Collier, David A. Freedman

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19Diagnostics Cannot Have Much Power Against General Alternatives

        ABSTRACT. Model diagnostics are shown to have little power unless alternative hypotheses can be narrowly defined. For example, independence of observations cannot be tested against general forms of dependence. Thus, the basic assumptions in regression models cannot be inferred from the data. Equally, the proportionality assumption in proportional-hazards models is not testable. Specification error is a primary source of uncertainty in forecasting, and this uncertainty will be difficult to resolve without external calibration. Model-based causal inference is even more problematic.

19.1. Introduction

        The object here is to sketch a demonstration that, unless additional ...

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