A causal chain is an ordered sequence of events in which any event in the chain causes the next. Some philosophers believe that causality relates facts, not events, in which case the meaning adjusts accordingly. Some philosophers believe that causality may not exist if determinism is true, since causality is simply the observation that one event precedes another, or that there is a pattern across space-time in which the events of one Similar type tend to correlate with events of another similar type (i.e. mass-energy distribution in space-time has a theoretical information "pattern" in which car accidents tend to correlate with injuries, for example).

What is causal chain analysis?

Causal Chain Analysis (CCA), often also called Root Cause Analysis (RCA), is closely related to systems thinking and the DPSIR approach. In its most basic form, a causal chain is an ordered sequence of events that link the causes of a problem with its effects. Each link in the causal chain is created by repeatedly answering the question Why? CCA is based on the belief that problems are best solved by trying to address, correct or eliminate the root causes, rather than simply addressing the immediately obvious symptoms. By targeting corrective actions at the root causes, a recurrence of the problem is more likely to be prevented. However, it is recognized that complete prevention of recurrence through corrective action is not always possible.

How is a causal chain analysis performed?

A causal chain is the path of influence that goes from the root cause to the symptoms of the problem. Each link in the chain represents something from the real world. At one end of the chain is the root cause. At the other extreme are the symptoms it causes. The many links between the two extremes are the intermediate causes. This is the causal chain of solution present in all problems. Popular approaches to solving the sustainability problem only see what is above the dashed line.

If you are working on a difficult problem, you have a superficial view of the problem. All you can see is the obvious: the black arrows. This leads to the shallow solutions trap of using shallow solutions to put pressure on low leverage points to resolve intermediate causes of the problem. Popular solutions are superficial because they fail to see below the dashed line at the fundamental layer, where the entire causal chain goes to the root causes. It's an easy trap to fall into because intuitively it seems that popular solutions like renewable energy, strict regulations, conservation, recycling, etc. should solve the sustainability problem. But they cannot, because they do not solve the root causes.

Implementing the principle

The fastest way to implement this principle is to develop the ability to see causal chains everywhere. Every time something interesting happens, ask yourself: Why did that happen? What was the root cause? Follow the causal chain relentlessly until you get to the root cause. It won't be long before you ask yourself "What is the root cause?" Ask about the sustainability issue or any major issue you are working on. The important thing is to always visualize a causal chain that goes from symptoms to root causes.

Do not direct your solutions to intermediate causes, as they will be superficial solutions that cannot solve the problem because they do not solve the root causes. By extending a causal chain to the solution of a problem, we arrive at a solution chain. A solution chain is the path of influence that goes from solutions to problem symptoms. In your analysis, you first find the causal chain of a problem. That gives you the root cause. Working backwards from that, then you find the chain of solutions.


Frydenberg (1990), Verma and Pearl (1991) and Spirtes, Glymour and Scheines (2000 (1993)), explain the fact that empirical data often considerably underdetermines causal inferences, especially when it comes to inferences from complex causal structures. It has become a widely recognized and researched problem in the causal (algorithmic) reasoning literature. All these studies support a theoretical framework according to which causal structures can be analyzed in terms of Bayesian networks.

Algorithms designed to discover causal Bayesian networks (hereinafter BN algorithms for short) analyze probabilistic input data, that is, probability distributions that are acquired, for example, from frequency distributions. The mapping of causal structures to probability distributions is not generally unequivocal. In many cases, BN algorithms assign more than one causal structure to a probability distribution.

Such ambiguities are not normally considered particularly surprising or worrisome in the literature, since clearly the causal inferences authorized by the corresponding empirical data depend crucially on the quality of the latter, which in the case of probabilistic data, such as well known, it can be adversely affected by many factors. For example, frequency distributions can present a considerable amount of confusing noise.

Ambiguities in the causal chain

If ambiguities in causal reasoning can be attributed, at least partially, to confounding noise in the data, the ambiguity ratio of a particular frequency distribution can be understood simply as an indicator of how close to the configuration behind a corresponding study has reached an ideal noise -free installation.

Mapping the causal chain

Data collection aspects

Causal chain mapping is a technique to help researchers better understand situational (for example, the mode of medication administration), environmental (for example, the prevalence of disease), and psychological (for example, emotions) causes. , values, beliefs) of medical compliance. As such, it requires researchers to collect information on a wide range of aspects of the treatment situation. This data can be collected through a variety of methods, ranging from standard responses to questionnaires to in-depth and projective techniques. To be more prominent, the data collection procedures should explicitly refer to particular circumstances, such as the doctor visit or the MDA process, as well as the personal characteristics of the individual.

Analysis aspects

A map of the causal chain is the end product of several stages of analysis. The first stage includes the analysis of the primary data, such as the quantitative analysis of a questionnaire or the identification of themes and patterns in qualitative data. Framework is a matrix development method for ordering, synthesizing, and analyzing data (Ritchie et al. 2003).

The framework requires the researcher to organize emerging themes from the individualized data into a detailed matrix that provides an opportunity for analysis by theme (vertically) or by individual (horizontally) (Ritchie et al. 2003). The personal characteristics described in the maps [age, gender, education and living environment (rural / urban)] arose from the hypotheses of what could have influenced the compliance with the LF treatment. The psychological traits included should preferably reflect the theoretical constructs of some standard health psychological approaches (Michie et al. 2005) to give the method internal consistency and comparability with other studies.

Bibliographic References

Baumgartner, Michael: Complex Causal Structures. Extensions of a Regularity Theory of Causation, Ph. D thesis, University of Bern 2006.

Glymour, Clark: A Review of Recent Work on the Foundations of Causal Inference, in: McKim, Vaughn R. and Turner, Stephen P., editors: Causality in Crisis? Notre Dame: University of Notre Dame Press 1997, 201–248.

Salmon, Wesley: Causality Without Counterfactuals, Philosophy of Science 61 (1994), 297–312.

Causal Chain

Causal Chain

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