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Manual Causation, Evidence, and Inference

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In addition, because of the complexity of ecological systems, the manipulations themselves may be confounded.

Causation in the social sciences: evidence,inference, and purpose

Contemporary philosophers who support manipulationist theories of causation have run into criticisms that the theories are circular, because they make manipulation more fundamental than causation, but manipulation is inherently causal. Further, the concept of manipulation seems anthropocentric. However, these criticisms may be avoided by treating manipulation as a sign or feature rather than a definition of causation and by allowing natural manipulations and even hypothetical manipulations Woodward Pearl models causal relationships as networks with nodes connected by equations.

The mechanism is the physical means by which a cause induces the effect. The physical mechanism can be thought of as a series of events at a lower level of organization than the cause and effect events. Enlightenment philosophers such as Leibniz and Laplace were metaphysical mechanists in that they considered the universe to be driven by physical interactions between entities.

Newton's theories, which involved action at a distance, supplanted that concept in physics, and the rise of quantum mechanics further diminished the concept of mechanism in physics. A known plausible mechanism has become one of the criteria for judging an empirical association to be causal in statistics Mosteller and Tukey and epidemiology Hill , Russo and Williamson , Susser a.

The idea behind Inference - evidence!

An alternative to the concept of mechanism presented here is the definition of mechanism as the chain or network of events that precede the effect Pearl , Simon and Rescher A conceptual problem with the concept of mechanisms of a cause is determining when is an event a mechanism for a cause and when may it be considered a cause itself? Simon and Rescher formally addressed this problem in terms of the causal ordering i. If we have a series of variables Vi that are dependent variables, then the last variable in the series that can be solved without solving for any of its successors can be treated as the cause.

This definition is effectively the opposite of the definition of mechanism used by us. That is, the mechanism is the series of events that lead up to—and include—the cause. The events between the cause and effect which are the mechanism in our sense can be ignored because, once an action has been taken, that action determines the causal event the occurrence of the effect.

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The most likely cause is the one that, when mathematically modeled, best fits and therefore best explains the data. This is, in theory, a very useful method. However, it requires that all causes be understood sufficiently to determine models and that data be available to parameterize the models. In addition, to statistically compare these models and identify the most likely cause, the same data set should apply to all alternatives. Otherwise, the relative likelihoods may be due to differences in the data applied to the models rather than the models themselves.

Finally, there must be enough data to allow the statistics to distinguish among the models. These conditions are often met for biological resource or pest management problems such as setting limits on fisheries but not for contaminated or disturbed sites. This approach began with Peirce's concept of the weight of evidence, which was revived by Good The weight of evidence for a hypothesis expressed as a mathematical relationship is the log of its likelihood relative to the likelihood of alternative hypotheses.

This Bayesian statistical approach for comparing models has been largely replaced by an information theoretic approach expressed as the relative magnitudes of Akaike's information criterion for each model Anderson However, there are multiple candidate causes, which should be reduced by defining the effect as specifically as possible. Complex causation may be minimized by carefully defining the set of agents and events that must combine to induce the effect.

Methods for Observational Data

All constituents of a complex cause that are necessary for the effect must be included—but not background conditions and trivial contributors see INUS. The point of casual analyses in CADDIS is to determine a sufficient intervention to eliminate the effect, not to completely define the agents in a system and their interactions.

Galileo recognized that there may be multiple i. He seems to imply an additive model of combined effects. Mill argued that the real cause of an effect is all antecedent conditions.


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This extreme of complex causation is in a sense monist. That is, there is only one cause, which is everything that has happened. In a metaphysical sense, most philosophers seem to agree with Mill Lewis However, other philosophers have developed various strategies for reducing this complexity to manageable but multicausal systems Lewis , Mackie , Pearl Network models represent causation graphically: nodes represent entities or states connected by arrows that represent models of individual causal processes or probabilities of the implied processes.

The advantages of network models are that, unlike equations, they convey directionality and make explicit the structure of interactions in multivariate causal relationships. Empirical methods for analyzing causal networks include path analysis, structural equation models, and Bayesian network analysis. Alternatively, a network can be modeled mechanistically through mathematical simulation e. Causal diagram theory, based on directed acyclic graphs, can be used to analyze complex causal relationships without parametric assumptions such as linearity that are required by structural equation modeling Pearl , Spirtes et al.

Statistical Language - Correlation and Causation

Network models require that causal relationships be known or at least hypothesized. Analysis may be used to test the plausibility of the network structure or to determine the relative strength of the contributions of nodes in the network to the effect of interest, given the assumed causal structure. In general, the causes of ecological impairments are not sufficiently well known to confidently define the network for a particular case, and data are insufficient to quantitatively analyze the network.

In addition, the application of network models to specific cases is problematical Pearl However, general network models might be used like other general models to support the credibility of specific causal hypotheses. The conceptual models in CADDIS could potentially be subject to quantitative analysis, and we have explored both Bayesian analysis and structural equation modeling.

We will continue to consider their utility. Analysis of causal networks began with Wright , , who developed path analysis basically, a combination of directed graphs and regression analysis to analyze the effects of genes and environment on phenotypes. It was first applied broadly by economists and social scientists beginning with Herbert Simon , where data sets are often large and include quantification of multiple causal factors. However, the most important technical developments and the most influential texts on causal networks come from the field of artificial intelligence Pearl , Spirtes et al.

Statistical analysis is now typically performed by an extension of path analysis called structural equation modeling. The techniques are now being applied in various fields, and their development is very active and controversial. However, even qualitative analyses of causal networks can help to identify potential confounders and aid in the design of studies Greenland et al. We agree that there are multiple legitimate definitions of causation and multiple methods of causal analysis. The CADDIS approach is conceptually pluralistic in that we use evidence corresponding to all potentially relevant theories and definitions of causation.

For example, we use evidence of association of C and E in the case, regularity of association in the region, and counterfactual evidence from experiments. However, we assume that in any real-world case, an effect has one cause which may be complex and that the different definitions are different representations of that actual relationship, not ontological alternatives. Since the late s, many philosophers, led by Nancy Cartwright , came to believe that none of the attempts to reduce causality to a particular definition counterfactual, probability raising, etc. Whether we accept ontological pluralism or not, we can use evidence to investigate causal relationships by applying the most appropriate concept of causality.

Causality just is the result of this epistemology. A causal hypothesis displays predictive performance if a prediction deduced from the hypothesis is confirmed by subsequent novel observations. Good predictive performance is considered by some to be an essential characteristic of a good scientific hypothesis or theory.

We believe that predicted observations are more powerful evidence than ad hoc causal explanations of existing observational data, because predictions can not be fudged. That is, one can invent an explanation for any observation after the fact to make it fit a preferred causal hypothesis, but, if a prediction is made before the observation, it cannot be changed afterward to fit the results. Schlick argued that the formation and verification of predictions provided a greater rigor for the regularity theory of causation. However, he identified two legitimate arguments for giving more weight to evidence that is predicted.

We are not metaphysical probabilists. We do not believe that the macrocosm things bigger than atoms is inherently random. Further, chaotic systems e. In practice, chaotic indeterminism is not distinguishable from other sources of noise in field data and does not significantly influence our ability to identify causes.

Because this metaphysical position implies that additional data collection and modeling can decrease uncertainty and drive probabilities of causation toward zero or one, CADDIS recommends iterative assessment when results are unclear. CADDIS does not suggest that probability raising constitutes a definition of causation because the apparent cause Co that is correlated with the effect may actually be correlated with the true cause Ct and methods to prevent confounding are unreliable. However, correlations and other expressions of the probability of association do provide evidence that can be useful in causal analyses.

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Karl Pearson was the father of causal probabilism. In The Grammar of Science, 3rd edition , Pearson argued that all we know of causation is the probability of association. In his arguments against smoking as a cause of lung cancer, Fisher pointed out that, in observational studies, correlation does not prove causation.

Confounding is always possible. A genetic trait may cause lung cancer and also make a person more susceptible to nicotine addiction, or nicotine craving may be a symptom of early stage lung cancer. Some modern philosophers have argued that determinism is untenable and, therefore, probability raising with temporal sequence is the best definition of causation Eells a, Eells b, Good , Good , Reichenbach , Suppes The major objection to probability raising is that it does not distinguish causal from non-causal associations Holland Cartwright argued that probability raising is a sort of symptom of causation rather than a definition.

Causal relationships result from interactions that are physical processes such as the exchange of energy or other conserved quantity e. Synonyms include process model , physicalist model , and mechanism. Many causes act through a physical process that exchanges some conserved quantity as in the philosophers' process theories of causation.

For example, the transfer of solar energy to the sediment of a shallow stream is followed by a transfer of thermal energy to the water and then to fish, causing the fish to leave in search of cooler water. However, many causal relationships are not easily expressed as such an exchange e. In such cases, it is more natural to speak of causal mechanisms rather than processes.

10 Things to Know About Causal Inference

When evidence of a process connection is available, it is treated as a variant of Evidence of Exposure or Biological Mechanism. Although Russell famously opposed the idea of causation, he attempted to develop a scientifically defensible theory of causation Russell However, he did not distinguish between causal processes and pseudo-processes Reichenbach , Salmon The modern process theory of causation was developed by Salmon , , Salmon's causation originally involved spatiotemporally continuous processes that transmit an abstract property termed a mark.

In response to Dowe he changed it to an exchange of invariant or conserved quantities such as charge, mass, energy, and momentum. However, some types of causation e. Numerous philosophers have published variants and presumed improvements on Salmon's and Dowe's process theory. Some psychologists and psycholinguists have adopted a version of the physical process theory of causation and argue based on experiments that people inherently assume that a process connection their terms are force dynamics or the dynamics model is involved in causal relationships Pinker , Wolff This causal intuition includes Salmon's and Dowe's physics but also implies by analogy, intrinsic tendencies, powers and even intentions.

Where and when the cause occurs, the effect always occurs. This definition is now usually modified by requiring that conditions be appropriate.