Diagnostics cannot have much power against general alternatives
In: International journal of forecasting, Band 25, Heft 4, S. 833-839
ISSN: 0169-2070
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In: International journal of forecasting, Band 25, Heft 4, S. 833-839
ISSN: 0169-2070
In: Evaluation review: a journal of applied social research, Band 30, Heft 6, S. 691-713
ISSN: 1552-3926
Experiments offer more reliable evidence on causation than observational studies, which is not to gainsay the contribution to knowledge from observation. Experiments should be analyzed as experiments, not as observational studies. A simple comparison of rates might be just the right tool, with little value added by "sophisticated" models. This article discusses current models for causation, as applied to experimental and observational data. The intention-totreat principle and the effect of treatment on the treated will also be discussed. Flaws in perprotocol and treatment-received estimates will be demonstrated.
In: Evaluation review: a journal of applied social research, Band 28, Heft 4, S. 267-293
ISSN: 1552-3926
This article (which is mainly expository) sets up graphical models for causation, having a bit less than the usual complement of hypothetical counterfactuals. Assuming the invariance of error distributions may be essential for causal inference, but the errors themselves need not be invariant. Graphs can be interpreted using conditional distributions, so thatwe can better address connections between the mathematical framework and causality in the world. The identification problem is posed in terms of conditionals. As will be seen, causal relationships cannot be inferred from a data set by running regressions unless there is substantial prior knowledge about the mechanisms that generated the data. There are fewsuccessful applications of graphicalmodels, mainly because few causal pathways can be excluded on a priori grounds. The invariance conditions themselves remain to be assessed.
In: Mathematical social sciences, Band 18, Heft 2, S. 192
In: International labour review, Band 123, S. 557-568
ISSN: 0020-7780
In: The journal of business, Band 54, Heft 3, S. 479
ISSN: 1537-5374
In: Journal of Contemporary Roman-Dutch Law, Band 79, S. 412-428
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In: A shorter version of this paper was presented at the Environmental Law Association 2013 Annual Conference at the Salt Rock Hotel in Salt Rock on 27 July 2013
SSRN
In: International labour review, Band 129, Heft 2, S. 165-184
ISSN: 0020-7780
In: Labour and society: a quarterly journal of the International Institute for Labour Studies, Band 8, Heft 2, S. 107-122
ISSN: 0378-5408
In: International labour review, Band 120, S. 751-763
ISSN: 0020-7780
In: International labour review, Band 117, Heft 1, S. 1-20
ISSN: 0020-7780
In: International labour review, Band 117, S. 1-20
ISSN: 0020-7780
In: International labour review, Band 112, S. 125-147
ISSN: 0020-7780
In: Evaluation review: a journal of applied social research, Band 32, Heft 4, S. 392-409
ISSN: 1552-3926
Regressions can be weighted by propensity scores in order to reduce bias. However, weighting is likely to increase random error in the estimates, and to bias the estimated standard errors downward, even when selection mechanisms are well understood. Moreover, in some cases, weighting will increase the bias in estimated causal parameters. If investigators have a good causal model, it seems better just to fit the model without weights. If the causal model is improperly specified, there can be significant problems in retrieving the situation by weighting, although weighting may help under some circumstances.