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16
From association to causation: Some remarks on the history of statistics
 Statist. Sci
, 1999
"... The “numerical method ” in medicine goes back to Pierre Louis ’ study of pneumonia (1835), and John Snow’s book on the epidemiology of cholera (1855). Snow took advantage of natural experiments and used convergent lines of evidence to demonstrate that cholera is a waterborne infectious disease. More ..."
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The “numerical method ” in medicine goes back to Pierre Louis ’ study of pneumonia (1835), and John Snow’s book on the epidemiology of cholera (1855). Snow took advantage of natural experiments and used convergent lines of evidence to demonstrate that cholera is a waterborne infectious disease. More recently, investigators in the social and life sciences have used statistical models and significance tests to deduce causeandeffect relationships from patterns of association; an early example is Yule’s study on the causes of poverty (1899). In my view, this modeling enterprise has not been successful. Investigators tend to neglect the difficulties in establishing causal relations, and the mathematical complexities obscure rather than clarify the assumptions on which the analysis is based. Formal statistical inference is, by its nature, conditional. If maintained hypotheses A, B, C,... hold, then H can be tested against the data. However, if A, B, C,... remain in doubt, so must inferences about H. Careful scrutiny of maintained hypotheses should therefore be a critical part of empirical work—a principle honored more often in the breach than the observance. Snow’s work on cholera will be contrasted with modern studies that depend on statistical models and tests of significance. The examples may help to clarify the limits of current statistical techniques for making causal inferences from patterns of association. 1.
Causal diagrams
, 2008
"... Abstract: From their inception, causal systems models (more commonly known as structuralequations models) have been accompanied by graphical representations or path diagrams that provide compact summaries of qualitative assumptions made by the models. These diagrams can be reinterpreted as probabil ..."
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Abstract: From their inception, causal systems models (more commonly known as structuralequations models) have been accompanied by graphical representations or path diagrams that provide compact summaries of qualitative assumptions made by the models. These diagrams can be reinterpreted as probability models, enabling use of graph theory in probabilistic inference, and allowing easy deduction of independence conditions implied by the assumptions. They can also be used as a formal tool for causal inference, such as predicting the effects of external interventions. Given that the diagram is correct, one can see whether the causal effects of interest (target effects, or causal estimands) can be estimated from available data, or what additional observations are needed to validly estimate those effects. One can also see how to represent the effects as familiar standardized effect measures. The present article gives an overview of: (1) components of causal graph theory; (2) probability interpretations of graphical models; and (3) methodologic implications of the causal and probability structures encoded in the graph, such as sources of bias and the data needed for their control.
On specifying graphical models for causation, and the identification problem
 Evaluation Review
, 2004
"... This paper (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 c ..."
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This paper (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 that we 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 few successful applications of graphical models, mainly because few causal pathways can be excluded on a priori grounds. The invariance conditions themselves remain to be assessed.
Causal models as minimal descriptions of multivariate systems. http://parallel.vub.ac.be/∼jan
, 2006
"... ABSTRACT. By applying the minimality principle for model selection, one should seek the model that describes the data by a code of minimal length. Learning is viewed as data compression that exploits the regularities or qualitative properties found in the data, in order to build a model containing t ..."
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Cited by 8 (0 self)
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ABSTRACT. By applying the minimality principle for model selection, one should seek the model that describes the data by a code of minimal length. Learning is viewed as data compression that exploits the regularities or qualitative properties found in the data, in order to build a model containing the meaningful information. The theory of causal modeling can be interpreted by this approach. The regularities are the conditional independencies reducing a factorization and the vstructure regularities. In the absence of other regularities, a causal model is faithful and offers a minimal description of a probability distribution. The causal interpretation of a faithful Bayesian network is motivated by the canonical representation it offers and faithfulness. A causal model decomposes the distribution into independent atomic blocks and is able to explain all qualitative properties found in the data. The existence of faithful models depends on the additional regularities in the data. Local structure of the conditional probability distributions allow further compression of the model. Interfering regularities, however, generate conditional independencies that do not follow from the Markov condition. These regularities has to be incorporated into an augmented model for which the inference algorithms are adapted to take into account their influences. But for other regularities, like patterns in a string, causality does not offer a modeling framework that leads to a minimal description. 1
Mining Causal Relationships in Multidimensional Time Series
"... Abstract. Time series are ubiquitous in all domains of human endeavor. They are generated, stored, and manipulated during any kind of activity. The goal of this chapter is to introduce a novel approach to mine multidimensional timeseries data for causal relationships. The main feature of the propos ..."
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Abstract. Time series are ubiquitous in all domains of human endeavor. They are generated, stored, and manipulated during any kind of activity. The goal of this chapter is to introduce a novel approach to mine multidimensional timeseries data for causal relationships. The main feature of the proposed system is supporting discovery of causal relations based on automatically discovered recurring patterns in the input time series. This is achieved by integrating a variety of data mining techniques. The main insight of the proposed system is that causal relations can be found more easily and robustly by analyzing meaningful events in the time series rather than by analyzing the time series numerical values directly. The RSST (Robust Singular Spectrum Transform) algorithm is used to find interesting points in every time series that is further analyzed by a constrained motif discovery algorithm (if needed) to learn basic events of the time series. The Grangercausality test is extended and applied to the multidimensional timeseries describing the occurrences of these basic events rather than to the raw timeseries data. The combined algorithm is evaluated using both synthetic and real world data. The real world application is to mine records of activities during a humanrobot interaction experiment in which a human subject is guiding a robot to navigate using free hand gesture. The results show that the combined system can provide causality graphs representing the underlying relations between the human’s actions and robot behavior that cannot be recovered using standard causal graph learning procedures.
Understanding of what engineers “do
 LSE Centre for Natural and Social Sciences, www.lse.ac.uk/Depts/cpnss/proj_causality.htm
, 2002
"... presented at ..."
Discovering Temporal/Causal Rules: A Comparison of Methods
 In Procs of AI 2003
, 2003
"... Abstract. We describe TimeSleuth, a hybrid tool based on the C4.5 classification software, which is intended for the discovery of temporal/causal rules. Temporally ordered data are gathered from observable attributes of a system, and used to discover relations among the attributes. In general, such ..."
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Abstract. We describe TimeSleuth, a hybrid tool based on the C4.5 classification software, which is intended for the discovery of temporal/causal rules. Temporally ordered data are gathered from observable attributes of a system, and used to discover relations among the attributes. In general, such rules could be atemporal or temporal. We evaluate TimeSleuth using synthetic data sets with wellknown causal relations as well as real weather data. We show that by performing appropriate preprocessing and postprocessing operations, TimeSleuth extends C4.5's domain of applicability to the unsupervised discovery of temporal relations among ordered data. We compare the results obtained from TimeSleuth to those of TETRAD and CaMML, and show that TimeSleuth performs better than the other systems. 1.
Causal Discovery Using Adaptive Logics. Towards a more realistic heuristics for human causal learning. ∗
, 2004
"... ..."
Statistical Models for Causation
, 2005
"... We review the basis for inferring causation by statistical modeling. Parameters should be stable under interventions, and so should error distributions. There are also statistical conditions on the errors. Stability is difficult to establish a priori, and the statistical conditions are equally probl ..."
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We review the basis for inferring causation by statistical modeling. Parameters should be stable under interventions, and so should error distributions. There are also statistical conditions on the errors. Stability is difficult to establish a priori, and the statistical conditions are equally problematic. Therefore, causal relationships are seldom to be inferred from a data set by running statistical algorithms, unless there is substantial prior knowledge about the mechanisms that generated the data. We begin with linear models (regression analysis) and then turn to graphical models, which may in principle be nonlinear.
Statistical Models for Causation: A Critical Review
"... Regression models are often used to infer causation from association. For instance, Yule [79] showed – or tried to show – that welfare was a cause of poverty. Path models and structural equation models are later ..."
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Regression models are often used to infer causation from association. For instance, Yule [79] showed – or tried to show – that welfare was a cause of poverty. Path models and structural equation models are later