Causal inference without counterfactuals (with Discussion)

By A. P. Dawid. 2000

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Causal inference by using invariant prediction: identification and confidence intervals (2015, background): “Among them are the framework of potential outcomes and counter-factuals [cf. Dawid, 2000, Rubin, 2005]; or structural equation modelling [cf. Bollen, 1989, Robins et al., 2000, Pearl, 2009] and corresponding graphical modeling [cf. Lauritzen and Spiegelhalter, 1988, Greenland et al., 1999, Spirtes…”
Causal Reasoning from Longitudinal Data * (2004, background): “As in the work of Pearl (1995, 2000) and Dawid (2000, 2002), our approach has been entirely probabilistic and the interpretation of probability is openly subjective.”
Causal Bounds and Observable Constraints for Non-deterministic Models (2012, methodology): “For certain applications though, for example, mutations that cause cancer (Aalen and Frigessi, 2007), subject matter knowledge suggests that assumptions about the existence of deterministic mechanisms are unrealistic and spawns controversy (Dawid, 2000).”
Influence Diagrams for Causal Modelling and Inference (2002, background): “The latter are closely related to counterfactual models, as discussed and criticised by Dawid (2000).”
Counterfactuals, Hypotheticals and Potential Responses: A Philosophical Examination of Statistical Causality (2006, background): “In Dawid (2000) I expressed serious doubts over existing CoE analyses, which I consider to be, at minimum, over-glib.”
Alternative Graphical Causal Models and the Identification of Direct E!ects (2010, methodology): “(2007); see also Geneletti and Dawid (2007) and the Appendix herein.”
Probabilities Of Causation: Three Counterfactual Interpretations And Their Identification (1999, methodology): “One may argue, of course, that if the coin is tossed after the bet, then it is not at all clear what our winning would be had we bet differently; merely uttering our bet could conceivably affect the trajectory of the coin (Dawid 1997).”
Single World Intervention Graphs ( SWIGs ) : A Unification of the Counterfactual and Graphical Approaches to Causality (2013, methodology): “2 In fact, if, following Geneletti and Dawid (2007) and Robins et al. (2007) 1 In (Pearl, 2000, 2009; Robins and Richardson, 2011) the acronym ‘NPSEM’ is used to refer to what is here termed an NPSEM-IE.”
Augmented designs to assess principal strata direct effects (2011, methodology): “into the sum of an NDE and an NIE: ATE=NIE.T/+NDE.C/ or ATE=NIE.C/+NDE.T/. Geneletti (2007) applied similar concepts by using a decision theoretic approach, where relationships between variables are encoded by using conditional independence statements, but without using counterfactuals (Dawid, 2000)”
Statistical Causality from a Decision-Theoretic Perspective (2014, background): “In Dawid (2000) I discussed the relationships and differences between these problems, and their requisite infrastructures, in some detail, and argued that, whereas (unlike for EoC) some form of counterfactual logic appears unavoidable for assessing CoE, the difficulties in appropriately modelling a…”
Identification and Likelihood Inference for Recursive Linear Models with Correlated Errors (2006, unknown): “The counterfactual interpretation is stronger, but consistent with the intervention-based approach , since the former models individual causal effects, while the latter only models average causal effects; see the discussion in Dawid [13] for a more detailed comparison of these frameworks.”