In: Quality & Quantity, 2015, vol. 49, no. 2, p. 839-856
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In: European Journal for Philosophy of Science, 2015, vol. 5, no. 1, p. 89-110
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In: Topoi, 2015, vol. 34, no. 1, p. 143-155
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In: Review of Philosophy and Psychology, 2015, vol. 6, no. 4, p. 761-778
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In: Biology & Philosophy, 2015, vol. 30, no. 5, p. 653-670
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(Working Papers SES ; 515)
This paper combines causal mediation analysis with double machine learning to control for observed confounders in a data-driven way under a selection-on- observables assumption in a high-dimensional setting. We consider the average indirect effect of a binary treatment operating through an intermediate variable (or mediator) on the causal path between the treatment and the outcome, as well as...
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In: Entropy, 2017, vol. 19, no. 7, p. 326-334
Computation models such as circuits describe sequences of computation steps that are carried out one after the other. In other words, algorithm design is traditionally subject to the restriction imposed by a fixed causal order. We address a novel computing paradigm beyond quantum computing, replacing this assumption by mere logical consistency: We study non-causal circuits, where a fixed time...
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In: SATS, 2016, vol. 17, no. 1, p. 61-80
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In: European Journal for Philosophy of Science, 2014, vol. 4, no. 2, p. 181-196
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In: Journal of Applied Econometrics, 2014, vol. 29, no. 6, p. 920-943
This paper demonstrates the identification of causal mechanisms of a binary treatment under selection on observables, (primarily) based on inverse probability weighting; i.e. we consider the average indirect effect of the treatment, which operates through an intermediate variable (or mediator) that is situated on the causal path between the treatment and the outcome, as well as the...
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