The Rule Of Cause & Effect In Daily Life

By Rajindra Clement Ratnapuli –APRIL 22, 2021

Rajindra Clement Ratnapuli

The notion of cause and effect or causation is not new, the concept has been debated for centuries by philosophers, scientists, and scholars of numerous disciplines. Not everyone is on board with the idea though. Bertrand Russell the renowned philosopher and mathematician argued that the idea of cause and effect is archaic and serves no useful purpose in science. Nevertheless, most of us have taken for granted the concept probably because in reality, it works. There are two approaches to deal with cause and effect. One is deterministic; If A causes B, then A must be followed by B – when A occurs B occurs. The second view is probabilistic causation. A can probabilistically cause B if A’s occurrence “raises” the probability of B. In everyday life, causation seems to be a deterministic concept very similar to the action-reaction rule in physics. Our life experiences and physical phenomena around us appear to be related to cause and effect. Even cosmological phenomena seem to bear a cause-and-effect relation suggesting that causation is perhaps universal. Causation is a deeply rooted concept of science and everyday life

The awareness to cause and effect in humans begins in the early stages of brain development. Even a year-old child would know how to press a button on his toy to listen to music. A grown-up kid need not be told to put on the wall switch to light up the room. Although cause comes first, it is the effect that we first notice. However, an exception seems to occur in the quantum world where the consequence may precede the cause (backward causality), or cause and effect may even occur simultaneously. But the understanding of this is still being developed and these weird quantum effects do diminish and rapidly converge to what we expect when dealing with real-world events. Clearly, a future cause or an arrival of an event cannot yield consequences in the present. For example, a future collision between an asteroid and the earth cannot bring disaster today. Also, not all causation is positive. In life, we have frequent encounters with negative causation (nonevents). If a houseplant is not regularly watered it will eventually wither away.

In practice, the difficult job is to find out the cause of an event. Once the cause is identified it becomes easier to understand and treat the consequences; a problem well stated (identified) is half solved. However, when the problem involves multiple causes and multiple effects, as it happens frequently in practice, the solution is likely to get more complicated. But there are analytical and empirical tools available to treat these situations. To give an example the Fishbone Diagram (Ishikawa Model) is quite popular in the industry (quality control, noncompliance, defect analysis, accident probes, product and process development). In the social sciences, causation is extensively used to study the gender pay gap, religious and ethnic conflicts, and marginalization, sexual discrimination, and poverty. A critical analysis of such problems will require the precise identification and full details of the cause and effect backed by reliable evidence. In medicine, symptoms and signs matter. But a symptom may not necessarily represent a real effect of a disease whereas a sign could be a good indicator. Another general feature of causation is that cause and effect may go sequentially in a chain. As an example, a family’s demise can lead to grieve, which can cause depression, and depression can lead to suicide and, so on.

In causation, there is also a statistical correlation between the cause (independent variable) and the effect (dependent variable). It implies that a change in the cause will bring a change to the effect. But the existence of a correlation does not necessarily mean there is causation or causal relationship between the two. For example, during warm weather, a beachside vendor finds that ice cream sales and the number of sunglasses sold show a positive correlation. However, the vendor cannot expect to increase the sunglass sales by asking people to eat more ice cream. This is a spurious relation and has no causality suggesting that there could be another variable affecting the correlation, which in this case is the outside temperature. Again, smoking leads to cancer, but cancer will not increase smoking. In everyday events, the appearance of a statistical correlation between cause and effect should be considered as a first step in the potential causation.

In statistical analysis of causation, it is essential to work out the statistical significance of the relationship. The scientific community will not accept causation unless the results have at least a 95% confidence level (giving a 5% or 1 in 20 chance of failure), which implies very high reliability. On the other hand, civil law requires only 51% proof of causation, giving a 49% chance of deviation from the results. When drafting policy, it is good practice always to rely on science-based evidence.

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