Cause and effect: Scientists develop a method to understand causality

Cause-effect relationships explain how one thing affects another. For example, if you are sitting on a chair and someone pulls you hard, you will fall. The fall was caused by the chair being pulled away, and the result was that you probably ended up on the floor with some scratches.

A group of scientists claim to have Developed the SURD method (Synergistic-Unique-Redundant Decomposition of Causation) To better understand how this system of causality works.

In a study published in the scientific journal Nature Communications, a team of engineers from the Massachusetts Institute of Technology (MIT) developed a technique that can be applied to different scenarios to identify potential variations in cause-effect phenomena.

Tracking one variable in cases of causality can be extremely complex, as some variables can create millions of variables.

The method is an algorithm that uses data collected over time to estimate the degree of impact of variables in a given situation. For example, Data on population changes in different marine animal species can help estimate the number of sardines in a particular area.

“The importance of our method lies in its interdisciplinary versatility. It can be applied to better understand the evolution of species in an ecosystem, the communication of neurons in the brain, and the interaction of climatic variables between regions, to name a few examples,” said MIT, Álvaro Martínez-Sánchez.

Cause and effect: variables

The method is an algorithm forming a ‘causation map’ and connecting possible variables of a cause-effect situation.

With these calculations, scientists can determine the nature of this relationship and evaluate whether one reaction will be affected by the other. Additionally, the technique estimates the degree of uncertainty in the outcome possibly resulting from unknown variables.

The researchers’ goal is to apply the model in the aerospace industry to identify features in aircraft design that will help better understand fuel consumption. So they can analyze the variables of this design and understand how they relate to the efficiency of the aircraft.

After further testing, the team realized that SURD could actually identify details of variables that could benefit the aerospace industry. However, more studies are needed to better understand its application potential in other fields.

“We hope that incorporating causality into models will help us better understand the relationship between an aircraft’s design variables and how this relates to efficiency. Part of our method detects if something is missing. We don’t know what’s missing but we know we need to add more variables to explain what’s going on”, said professor Adrián Lozano-Durán in an official statement.

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