Model-Contingent Interpretation of Evidence

forensic

Model-Contingent Interpretation of Evidence

This project considers issues that arise when data are generated by some process of which there is imperfect knowledge and one wishes to extract information about some feature of the process from the data it generates. The following issues are addressed.

  • What knowledge of a process is in principle obtainable from the data (evidence) it generates? Models of processes provide restrictions which can be sufficient to identify features of a data generating process, that is structural features. We study the nature of the restrictions that are required to identify interesting features and seek to determine minimally restrictive models for particular structural features.
  • How can data be processed to give information about identified structural features? We study methods for estimation and inference in the context of models embodying weak identifying restrictions.
  • Can models be falsified? There may exist a model that identifies a structural feature which embodies restrictions so weak that the model is non-falsifiable. Conclusions drawn from processing evidence through such a model must be contingent on the veracity of the restrictions embodied in the model. We study the characteristics of non-falsifiable models that identify interesting structural features and how the existence of more than one distinct non-falsifiable model bears on the interpretation of evidence.

The project builds on a stream of research on the subject of identification and inference started in econometrics in the 1920’s and pursued since in economics and other areas of social science.

Project Investigators:

Andrew Chesher
Hidehiko Ichimura
Sokbae Lee

Visitors to the project with interests in this research agenda:

Date
Title
First Author
Publication Type
(No date) Nonparametric identification under discrete variation - cemmap Working Paper 19/03 Andrew Chesher Working paper
(No date) Semiparametric identification in duration models - cemmap Working Paper 20/02 Andrew Chesher Working paper
(No date) Instrumental Values - cemmap Working Paper CWP17/02 Andrew Chesher Working paper
(No date) Characterization of the asymptotic distribution of semiparametric M-estimators Hidehiko Ichimura Working paper
(No date) Nonparametric instrumental variables estimation of a quantile regression model (joint with Joel L. Horowitz) Sokbae 'Simon' Lee Working paper
(No date) Ability, sorting and wage inequality (joint with Pedro Carneiro) Sokbae 'Simon' Lee Working paper
(No date) Identification of a competing risks model with unknown transformations of latent failure times Sokbae 'Simon' Lee Working paper
(No date) Reform of unemployment compensation in Germany: a nonparametric bounds analysis using register data (joint with Ralf A. Wilke) Sokbae 'Simon' Lee Working paper
20/06/2006 Counterfactuals, Hypotheticals and Potential Responses: A Philosophical Examination of Statistical Causality Philip Dawid Article in Journal
01/11/2007 Identifying the environmental causes of disease: how should we decide what to believe and when to take action? Philip Dawid Book
20/12/2005 Identifying the consequences of dynamic treatment strategies Philip Dawid Technical report
01/06/2006 Direct and indirect effects of sequential treatments Vanessa Didelez Technical report
05/10/2006 Identification Analysis and Evidence Science Andrew Chesher Other
12/01/2004 Causality: Some References on Probabilistic Causal Modelling & Inference Philip Dawid Other
25/08/2006 Some aspects of statistical inference from non-experimental data Philip Dawid Other

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