Probabilistic Theory Of Causation Research Articles

Conducting Causal Analysis by Means of Approximating Probabilistic Truths.

Simple SummaryThe current paper develops a probabilistic theory of causation and suggests practical routines for conducting causal inference applicable to new machine learning methods that have, so far, remained relatively underutilized in this context.The current paper develops a probabilistic theory of causation using measure-theoretical concepts and suggests practical routines for conducting causal inference. The theory is applicable to both linear and high-dimensional nonlinear models. An example is provided using random forest regressions and daily data on yield spreads. The application tests how uncertainty in short- and long-term inflation expectations interacts with spreads in the daily Bitcoin price. The results are contrasted with those obtained by standard linear Granger causality tests. It is shown that the suggested measure-theoretic approaches do not only lead to better predictive models, but also to more plausible parsimonious descriptions of possible causal flows. The paper concludes that researchers interested in causal analysis should be more aspirational in terms of developing predictive capabilities, even if the interest is in inference and not in prediction per se. The theory developed in the paper provides practitioners guidance for developing causal models using new machine learning methods that have, so far, remained relatively underutilized in this context.

Modeling Cumulative Biological Phenomena with Suppes-Bayes Causal Networks.

Several diseases related to cell proliferation are characterized by the accumulation of somatic DNA changes, with respect to wild-type conditions. Cancer and HIV are 2 common examples of such diseases, where the mutational load in the cancerous/viral population increases over time. In these cases, selective pressures are often observed along with competition, co-operation, and parasitism among distinct cellular clones. Recently, we presented a mathematical framework to model these phenomena, based on a combination of Bayesian inference and Suppes’ theory of probabilistic causation, depicted in graphical structures dubbed Suppes-Bayes Causal Networks (SBCNs). The SBCNs are generative probabilistic graphical models that recapitulate the potential ordering of accumulation of such DNA changes during the progression of the disease. Such models can be inferred from data by exploiting likelihood-based model selection strategies with regularization. In this article, we discuss the theoretical foundations of our approach and we investigate in depth the influence on the model selection task of (1) the poset based on Suppes’ theory and (2) different regularization strategies. Furthermore, we provide an example of application of our framework to HIV genetic data highlighting the valuable insights provided by the inferred SBCN

Cost overruns in transportation infrastructure projects: Sowing the seeds for a probabilistic theory of causation

Understanding the cause of cost overruns in transportation infrastructure projects has been a topic that has received considerable attention from academics and the popular press. Despite studies providing the essential building blocks and frameworks for cost overrun mitigation and containment, the problem still remains a pervasive issue for Governments worldwide. The interdependency that exists between ‘causes’ that lead to cost overruns materialising have largely been ignored when considering the likelihood and impact of their occurrence. The vast majority of the cost overrun literature has tended to adopt a deterministic approach in examining the occurrence of the phenomenon; in this paper a shift towards the adoption of pluralistic probabilistic approach to cost overrun causation is proposed. The establishment of probabilistic theory incorporates the ability to consider the interdependencies of causes so to provide Governments with a holistic understanding of the uncertainties and risks that may derail the delivery and increase the cost of transportation infrastructure projects. This will further assist in the design of effective mitigation and containment strategies that will ensure future transportation infrastructure projects meet their expected costs as well as the need of taxpayers.

How Probabilistic Causation Can Account for the Use of Mechanistic Evidence

In a recent article in this journal, Federica Russo and Jon Williamson argue that an analysis of causality in terms of probabilistic relationships does not do justice to the use of mechanistic evidence to support causal claims. I will present Ronald Giere’s theory of probabilistic causation, and show that it can account for the use of mechanistic evidence (both in the health sciences—on which Russo and Williamson focus—and elsewhere). I also review some other probabilistic theories of causation (of Suppes, Eells, and Humphreys) and show that they cannot account for the use of mechanistic evidence. I argue that these theories are also inferior to Giere’s theory in other respects.

Causal Generalizations and Good Advice

In this essay, I focus on the convergence of two problems. The first is our old nemesis, the reference-class problem. The second is the problem of interpreting causal generalizations such as 'smoking causes lung cancer'. This problem has been with us since the time of Hume. Within the framework of the probabilistic theory of causation (which I have been exploring since the early '90s), this problem takes on a very precise form. All parties to the dispute agree that the truth of such a generalization depends upon certain conditional probabilities; they disagree about the configuration of these probabilities that must obtain in order for the claim to be true. The key to solving this second problem rests in the observation that causal generalizations can serve as guides to life: if I know that smoking causes lung cancer, that gives me a good reason not to smoke. Here is where the two problems converge. One way to formulate the reference-class problem is as follows: which objective, conditional probabilities should guide my decisions? I answer that one should be (partly) guided by the conditional probabilities that figure in the probabilistic theory of causation. This, in turn, helps us to solve the second problem. 'Smoking causes lung cancer' describes a configuration of objective, conditional probabilities, such that if the agent were guided by those probabilities, it would be rational for her to refrain from smoking. Like Hume, I conclude that the truth of a causal generalization is partly a subjective matter.

The role of contrast in causal and explanatory claims

Following Dretske (1977), there has been a considerable body of literature on the role of contrastive stress in causal claims. Following van Fraassen (1980), there has been a considerable body of literature on the role of contrastive stress in explanations and explanation-requesting why-questions. Amazingly, the two bodies of literature have remained almost entirely disjoint. With an understanding of the contrastive nature of ordinary causal claims, and of the linguistic roles of contrastive stress, it is possible to provide a unified account of both phenomena. I provide such an account from within the framework of a probabilistic theory of causation. Relations of 'screening-off', long familiar to researchers in probabilistic causality, play a central role in this account.

A probabilistic theory of second order causation

Larry Wright and others have advanced causal accounts of functional explanation, designed to alleviate fears about the legitimacy of such explanations. These analyses take functional explanations to describe second order causal relations. These second order relations are conceptually puzzling. I present an account of second order causation from within the framework of Eells' probabilistic theory of causation; the account makes use of the population-relativity of causation that is built into this theory.

A generalized probabilistic theory of causal relevance

I advance a new theory of causal relevance, according to which causal claims convey information about conditional probability functions. This theory is motivated by the problem of disjunctive factors, which haunts existing probabilistic theories of causation. After some introductory remarks, I present in Section 3 a sketch of Eells's (1991) probabilistic theory of causation, which provides the framework for much of the discussion. Section 4 explains how the problem of disjunctive factors arises within this framework. After rejecting three proposed solutions, I offer in Section 6 a new approach to causation that avoids the problem. Decision-theoretic considerations also support the new approach. Section 8 develops the consequences of the new theory for causal explanation. The resulting theory of causal explanation incorporates the new insights while respecting important work on scientific explanation by Salmon (1971), Railton (1981), and Humphreys (1989). My conclusions are enumerated in Section 9.

Remarks on Simon's Comments

Cognitive ScienceVolume 15, Issue 2 p. 301-303 Free Access Remarks on Simon's Comments Yoav Shoham, Corresponding Author Yoav Shoham Stanford UniversityDepartment of Computer Science, Stanford University, Stanford, CA 94305-2095Search for more papers by this author Yoav Shoham, Corresponding Author Yoav Shoham Stanford UniversityDepartment of Computer Science, Stanford University, Stanford, CA 94305-2095Search for more papers by this author First published: April 1991 https://doi.org/10.1207/s15516709cog1502_6Citations: 8AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL REFERENCES de Kleer, J., & Brown, J.S. (1984). Qualitative physics based on confluences. Artificial Intelligence, 24. Iwasaki, Y., & Simon, H.A. (1986). Causality in device behavior. Artificial Intelligence, 29. D. Lerner (Ed.). (1965). Cause and effect. Toronto: Collier Macmillan. Shoham, Y. (1990). Nonmonotonic reasoning and causality. Cognitive Science, 14, 213– 252. Simon, H.A. (1991). Nonmonotonic reasoning and causation: Comment. Cognitive Science, 15, 293– 300. Suppes, P. (1970). A probabilistic theory of causation. Amsterdam: North Holland. Citing Literature Volume15, Issue2April 1991Pages 301-303 ReferencesRelatedInformation

Causality and statistics

Many aspects of statistical design, modelling, and inference have close and important connections with causal thinking. These are analyzed in the paper against a philosophical background that regards formal mathematical models as having dual interpretations, reflecting both objectivist reality and subjectivist rationality. The latter aspect weakens the need for an objective theory of probabilistic causation, and suggests that a traditional image of causes as deterministic mechanisms should remain primary. It is argued that such causes should guide much preformal thinking about what to include in formal statistical models, especially of dynamic phenomena. The statistical measurement of causal effects is facilitated by good statistical design, including randomization where feasible, and requires other methodologies for controlling and assessing uncertainties, for example in model construction and inference. Illustrative examples include case studies where the problem is to assess retrospectively the causes of observed events and where the task is to assess future risks from controllable factors.

Concerning a probabilistic theory of causation adequate for the causal theory of time

Concerning a probabilistic theory of causation adequate for the causal theory of time