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How to go from correlation to causation? Uncover the mystery of causal inference!
Causal inference is a crucial process in scientific research, which aims to determine the actual independent influence of a particular phenomenon on other phenomena in a larger system. Although correlation analysis can show a relationship between the two, it cannot establish a clear cause-and-effect relationship. The process of causal inference allows us to analyze the response of an effect variable when the cause of the effect variable changes.
Causal inference provides evidence of causal relationships, and this evidence is derived from causal reasoning.
The challenge with this inferential approach is that experiments in many scientific fields are often difficult to conduct. With the methodological innovations in recent years, causal inference has been widely used in various scientific fields. However, in some areas, especially those where human intervention is difficult, causal inference remains a complex topic.
Definition of causal inference
At its core, causal inference is a simple definition: determining whether one thing happens because of another thing. This process involves three important conditions:
- There is a covariation relationship between cause and effect.
- Causal temporal order, where the cause occurs before the effect.
- Eliminate other possible alternative theories.
Methodology of causal inference
General Methods
Causal inference is usually made by studying a system in which the measurement of one variable is suspected to affect another variable. This requires the use of the scientific method, which begins with formulating a falsifiable null hypothesis and then testing it using statistical methods. Whether using frequentist statistical inference or Bayesian inference, the core is to identify the fundamental source of data variation.
Experimental and quasi-experimental methods
The true causal mechanism can be verified through experimental methods. The main motivation of an experiment is to manipulate only the variable of interest while keeping all other variables constant. If an experiment produces a statistically significant effect, then a causal effect of the manipulated variable can be inferred.
When traditional experimental methods are difficult to implement, quasi-experimental methods provide an alternative option for verifying causal mechanisms.
Quasi-experimental methods are often used when experiments are costly or impossible to conduct. For example, experiments involving large systems or potential hazards can achieve similar results using quasi-experimental designs.
Causal Inference in Epidemiology
Epidemiology is the study of patterns of health and disease among populations in order to infer cause and effect. Although an association between exposure to a putative risk factor and a disease may indicate causation, Hill's criteria and Koch's test still provide a historically useful framework for assessing causality. sex.
Causal Inference in Social Sciences
The social sciences are increasingly moving toward incorporating quantitative frameworks to assess causality. Gary King et al.'s 1994 book Designing Social Surveys emphasized the combination of quantitative and qualitative methods. This process helps to improve the methodological rigor of social science research.
Scientific Ethics and the Challenge of Causal Inference
Despite significant progress in the methodology of causal inference, many challenges remain in truly establishing causal relationships, often due to the complexity of the system and the misconduct of some scholars. For example, many social science studies may mislead descriptive analysis into causal analysis, lacking a correct interpretation of the true causal path.
Causal inference requires multi-faceted consideration and verification, rather than relying solely on a single descriptive data.
These challenges remind us that scientific research must remain rigorous and transparent to avoid misleading conclusions. Although there are still many unknowns and challenges in causal inference, these processes have prompted scientists to reflect on and improve different inference methods in order to more accurately understand the nature of the world.
So, in the process of understanding causal inference, how can we more effectively distinguish correlation from causation?
