What is the difference between cause and association

If men were randomly assigned to some jobs formerly held by women, would there be interactions across units that would violate SUTVA? Not surprisingly, if the SUTVA assumption fails, then it will be at best hard to generalize the results of an experiment and at worst impossible to even interpret its results.

Generalization is hard if, for example, imposing a policy of welfare time-limits on a small group of welfare recipients has a much different impact than imposing it upon every recipient.

Perhaps the imposition of limits on the larger group generates a negative attitude toward welfare that encourages job seeking which is not generated p. In both cases, the pattern of assignment to treatments seems to matter as much as the treatments themselves because of interactions among the units, and the interpretation of these experiments might be impossible because of the complex interactions among units.

If SUTVA does not hold, then there are no ways such as randomization to construct closest possible worlds, and the difficulty of determining closest possible worlds must be faced directly. If SUTVA holds and if there is independence of assignment and outcome through randomization, then the degree of causal connection can be estimated. Much of the art in experimentation goes into strategies that will increase the likelihood that they do hold.

Cases can be isolated from one another to minimize interference, treatments can be made as uniform as possible, and the characteristics and circumstances of each case can be made as uniform as possible, but nothing can absolutely ensure that SUTVA and the independence of assignment and outcome hold. If noninterference across units SUTVA holds and if independence of assignment and outcome hold, then mini-closest-possible worlds have been created which can be used to compare the effects in a treatment and control condition.

The mathematical conditions required for the third method to work follow easily from the Neyman—Holland—Rubin setup, but there is no method for identifying the proper covariates. And outside of experimental studies, there is no way to be sure that conditional independence of assignment and outcome holds. Even if we know about something that may confound our results, we may not know about all things, and without knowing all of them, we cannot be sure that correcting for some of them p. Thus observational studies face the problem of identifying a set of variables that will ensure conditional independence so that the impact of the treatment can be determined.

A great deal of research, however, does this in a rather cavalier way. Additional information is needed to rule out spurious correlation and to establish the direction of causation. Appeal can be made to temporal precedence or to what was manipulated to pin down the direction of causation, but neither of these approaches provides full protection against common cause.

More experiments or observations which study the impact of other variables which suppress supposed causes or effects may be needed, and these have to be undertaken imaginatively in ways that explore different possible worlds.

Abbott, A. Sequences of social events. Historical Methods , — Find this resource:. From causes to events. Sociological Methods and Research , — Sequence analysis: new methods for old ideas.

Annual Review of Sociology , 93— Achen, C. Toward theories of data: the state of political methodology. Bartels, L. The state of quantitative political methodology. Beauchamp, T. Hume and the Problem of Causation. New York: Oxford University Press. Bennett, J. Events and Their Names. Indianapolis: Hackett. Berger, P. Garden City, NY: Anchor. Brady, H. New York: Rowman and Littlefield. Law and data: the butterfly ballot episode.

PS: Political Science and Politics , 59— Campbell, D. Experimental and Quasi-Experimental Designs for Research. Chicago: Rand McNally. Cartwright, N. Cook, T. Boston: Houghton Mifflin. The causal assumptions of quasi-experimental practice. Synthese , — Cox, D. The Planning of Experiments. New York: Wiley. Causality: some statistical aspects.

Davidson, D. Essays on Actions and Events , 2nd edn. Oxford: Clarendon Press. Dessler, D. Beyond correlations: toward a causal theory of war.

International Studies Quarterly , — Dilthey, W. New York: Harper. Durkheim, E. The Rules of Sociological Method. New York: Free Press. Elster, J. A plea for mechanisms. In Social Mechanisms , ed. Cambridge: Cambridge University Press. Ehring, D. Causation and Persistence: A Theory of Causation. Fearon, J. Counterfactuals and hypothesis testing in political science. World Politics , — Fisher, R. Statistical Methods for Research Workers. Edinburgh: Oliver and Boyd.

The arrangement of field experiments. Journal of the Ministry of Agriculture , — The Design of Experiments. Freedman, D. As others see us: a case study in path analysis. Journal of Educational Statistics , —, with discussion. Statistical models and shoe leather. Sociological Methodology , — From association to causation via regression. McKim and S. Turner, Notre Dame, Ind. From association to causation: some remarks on the history of statistics.

Statistical Science , — Gasking, D. Causation and recipes. Mind , — Glennan, S. Mechanisms and the nature of causation. Erkenntnis , 49— Goldthorpe, J. Causation, statistics, and sociology. European Sociological Review , 1— Goodman, N. The problem of counterfactual conditionals. Journal of Philosophy , — Oxford: B. Hausman, D. Causal Asymmetries. New York: Cambridge University Press. Heckman, J. Sample selection bias as a specification error.

Econometrica , — Hempel, C. Aspects of Scientific Explanation. Hill, A. The environment and disease: association or causation? Proceedings of the Royal Society of Medicine , — Holland, P. Statistics and causal inference in theory and methods. Journal of the American Statistical Association , — Hume, D. A Treatise of Human Nature , ed. Selby-Bigge and P. An Enquiry Concerning Human Understanding , ed. Kitcher, P. Van Fraassen on explanation. Journal of Philosopy , — Conceptual metaphor in everyday language.

Journal of Philosophy , 77 8 : — Metaphors We Live By. Chicago: University of Chicago Press. New York: Basic Books. Lewis, D. Cambridge, Mass: Harvard University Press. Philosophical Papers , vol. Machamber, P. Thinking about mechanisms. Philosophy of Science , 1— Mackie, J. Causes and conditions. American Philosophical Quarterly , 2: — Marini, M. Causality in the social sciences. Mauldon, J.

Mellors, D. The Facts of Causation. London: Routledge. Menzies, P. Causation as a secondary quality. British Journal for the Philosophy of Science , — Mill, J. A System of Logic, Ratiocinative and Inductive , 8th edn. New York: Harper and Brothers. Neyman, J. On the application of probability theory to agricultural experiments: essay on principles, trans. Dabrowska and T. Statistical Science , 5: —80; first pub.

Papineau, D. Causal asymmetry. Pearl, J. Causality: Models, Reasoning, and Inference. Pearson, K. The Grammar of Science , 3rd edn.

London: Adam and Charles Black. Pierson, P. Ragin, C. Berkeley: University of California Press. Riker, W. Events and situations. Journal of Philosophy , 57— Rubin, D. Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology , — Bayesian inference for causal effects: the role of randomization. Annals of Statistics , 6: 34— Statistics and casual inference: comment: which ifs have casual answers. Comment: Neyman and causal inference in experiments and observational studies.

Statistical Science , 5: — Russell, B. On the notion of cause. In Mysticism and Logic and Other Essays. New York: Longmans, Green. Salmon, W.

Four Decades of Scientific Explanation. Minneapolis: University of Minnesota Press. London: Cambridge University Press. The Construction of Social Reality. Shafer, G. The Art of Casual Conjecture. Cambridge, Mass. Simon, H. On the definition of the causal relation. Causal ordering, comparative statics, and near decomposability. Journal of Econometrics , — Sobel, M. Causal inference in the social and behavioral sciences.

Arminger, C. Clogg, and M. New York: Plenum. Sorenson, A. Theoretical mechanisms and the empirical study of social processes. Sosa, E. Spellman, B. When possibility informs reality: counterfactual thinking as a cue to causality. Current Directions in Psychological Science , 8: —3. Sprinzak, E. History and Theory , — Tetlock, P.

Tilly, C. New York: Russell Sage Foundation. The Scientific Image. Explanation and Understanding. Causality and Determinism.

New York: Columbia University Press. Wand, J. American Political Science Review , — Wawro, G. Weber, M. Selections in Translation , ed. Runciman, trans. Wendt, A. Social Theory of International Politics.

A leading proponent of this approach is Bas van Fraassen Pearl provides a comprehensive approach to causality rooted in a Bayesian perspective. Shafer links decision theory and causal trees in a novel and useful way. Modal logics, which try to capture the nature of necessity, possibility, contingency, and impossibility, have been developed for counterfactuals Lewis a ; b.

These logics typically judge the truthfulness of the counterfactual on whether or not the statement would be true in the most similar possible world where the premise is true. Problems arise, however, in defining the most similar world. All events seem entirely loose and separate. One event follows another; but we never can observe any tye between them. They seem conjoined , but never connected. And as we can have no idea of any thing, which never appeared to our outward sense or inward sentiment, the necessary conclusion seems to be, that we have no idea of connexion or power at all, and that these words are absolutely without meaning, when employed either in philosophical reasonings, or common life….

This connexion, therefore, we feel in the mind, this customary transition of the imagination from one object to its usual attendant, is the sentiment or impression, from which we form the idea of power or necessary connexion.

For a thorough discussion see Beauchamp and Rosenberg Is this category anything but a conceptual limit to experience, and without any basis in perception beyond a statistical approximation? One approach emphasizes agency and manipulation. The other approach emphasizes mechanisms and capacities. The major difference is the locus of the underlying force that defines causal relationships.

As time went on, the amount of studies that showed this association accumulated and the collective evidence gave strong indications that lung cancer was causally related to cancer. Mounting data from observational studies eventually pressured the government into recommending that people stop smoking. This is an example of where an association may be very tightly correlated and reproducible in different populations, and so gives enough evidence for people to act. However, situations like this are rare and problems come when associations are inappropriately portrayed as causation.

The best way to prove a definitive cause, particularly for a medicine or intervention, is by conducting a randomised controlled trial. A randomised controlled trial is a type of study that looks at occurrence of outcomes in different groups which are selected in such a way that confounding factors are unlikely to have an impact on the result.

Imagine factor 1 is a treatment and factor 2 is the number of people experiencing a particular symptom. Whether or not participants receive the treatment factor 1 should be the only difference between the two groups. Ideally, everything else about the groups should be exactly the same: their age, their sex, their ethnicity, their long-standing health, the food they eat, the time they wake up, the relationships they have, absolutely everything.

This way, we would know that the change in factor 2, i. We live in a world where everybody is different and it is impossible to ensure, with complete certainty, that no other external factor is causing a change in factor 2.

To overcome this, we try to make sure that the people in each group are as similar as possible by randomising them to different groups so that the many variations between people are equally spread — effectively cancelling each other out.

Then, we try to minimise the effect of external factors by ensuring that the only thing which changes between the groups is exposure to the treatment. By controlling all factors, other than the variable we want to study, we can say with reasonable certainty that there is indeed a causative link between the two factors.

When reading an article that says a treatment or lifestyle factor is associated with better outcomes, be wary. The people who seek and receive a treatment may be healthier and have better living conditions than those who do not. Therefore, people receiving the treatment might appear to benefit, but the difference in outcomes could be because they are healthier and have better living conditions.

There are dozens of ways in which external factors can influence experimental results, even in a clinical trial. Disentangling cause from association is a tricky business and it takes a brave person to claim that they can definitively prove one factor causes another. What you should take away from this is a healthy dose of scepticism. Ask: is what you have an association or a cause? If the correlation coefficient has a positive value above 0 it indicates a positive relationship between the variables meaning that both variables move in tandem, i.

Where the correlation coefficient is 0 this indicates there is no relationship between the variables one variable can remain constant while the other increases or decreases. While the correlation coefficient is a useful measure, it has its limitations: Correlation coefficients are usually associated with measuring a linear relationship. For example, if you compare hours worked and income earned for a tradesperson who charges an hourly rate for their work, there is a linear or straight line relationship since with each additional hour worked the income will increase by a consistent amount.

If, however, the tradesperson charges based on an initial call out fee and an hourly fee which progressively decreases the longer the job goes for, the relationship between hours worked and income would be non-linear , where the correlation coefficient may be closer to 0. Care is needed when interpreting the value of 'r'. It is possible to find correlations between many variables, however the relationships can be due to other factors and have nothing to do with the two variables being considered.

For example, sales of ice creams and the sales of sunscreen can increase and decrease across a year in a systematic manner, but it would be a relationship that would be due to the effects of the season ie hotter weather sees an increase in people wearing sunscreen as well as eating ice cream rather than due to any direct relationship between sales of sunscreen and ice cream.

The correlation coefficient should not be used to say anything about cause and effect relationship. By examining the value of 'r', we may conclude that two variables are related, but that 'r' value does not tell us if one variable was the cause of the change in the other. For example, there is a statistical association between the number of people who drowned by falling into a pool and the number of films Nicolas Cage appeared in in a given year.

However, there is obviously no causal relationship. Jewish women have a higher risk of breast cancer, while Mormons have a lower risk. However, one's religion is not a cause of breast cancer. There are other explanations.

It has been convincingly demonstrated that people of lower socioeconomic status SES have a higher risk of lung cancer, i. A more plausible explanation is that people of lower SES are more likely to smoke and to be chronically exposed to air pollution and that exposure of the respiratory tract to these contaminants causes mutations in bronchial cells that can eventually produce a cancer.