Causal Inference without Counterfactuals

By A. P. Dawid. 2000

525 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability, Causal Inference & Discovery

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Citations in the corpus

Counterfactual Fairness (2017, background): “Although this is well understood, it is worthwhile remembering that causal models always require strong assumptions, even more so when making counterfactual claims [8].”
On formalizing fairness in prediction with machine learning (2017, background): “Moreover, it has been argued that counterfactual reasoning may negatively influence the process of identifying causality (39; 9).”
Causal Reasoning for Algorithmic Fairness (2018, background): “There is a clear advantage on targeting group eﬀects, since they do not require postulating an unobservable joint distribution of two or more outcomes which can never be observed together, where even the existence of counterfactuals can justiﬁably be treated as a metaphysical concept [11].”
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 interpretation of stochastic differential equations (2013, background): “usal structure of the system. We do not directly provide such a representation of causality for SDEs. In general, the precise meaning of “causation” is a point of contemporary debate, see for example [7]. For our purposes, it suﬃces to take a practical standpoint: The causal structure of a system is”
Causal Structure Learning (2017, background): “The problematic aspect is clearly that the realizations of the noise under diﬀerent interventions can never be observed simultaneously and any statement about the joint distribution of the noise under diﬀerent interventions is thus in principle unfalsiﬁable and untestable (Dawid, 2000).”
Estimating high-dimensional intervention effects from observational data (2008, background): “This task coincides with inferring causal eﬀects, a well-established area in Statistics (e.g., [5, 8, 10, 11, 13, 18, 24, 25, 26] and [31]).”
Toward a Unified Theory of Causality (2008, background): “The same idea is varyingly called “average treatment effect” (Sobel, 1996), “average causal response” (Angrist & Imbens, 1995), and “average causal effect” (Dawid, 2000).”
Margins of discrete Bayesian networks (2015, background): “functions fv are a purely mathematical construct, and thus philosophical questions about the nature and existence of potential outcomes have no direct bearing on the results herein (see, for example, Dawid, 2000). 24 6 Main Results In this section we provide the technical results to prove the main t”
Causal Analysis of Agent Behavior for AI Safety (2021, background): “The downside is that such counterfactuals do not make empirically verifiable claims (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).”
Quality Meets Quantity: Case Studies, Conditional Probability, and Counterfactuals (2004, background): “See Campbell and Stanley 1966 for an overview particularly relevant for the social sciences.”
Causality in Economics (2006, methodology): “An example, adapted from Dawid [4], makes this point.”
Experimental designs for identifying causal mechanisms (2013, background): “The relative advantages and disadvantages of the counterfactual versus non-counterfactual approaches to causal inference are beyond the scope of the current paper (see Dawid, 2000).”
Causal inference (2011, background): “Pearl [27] in his comments on Dawid [7] made this point explicitly and clearly.”