the methods

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RCT is considered the gold standard in research for establishing causal relationships between a treatment or intervention and an outcome.RCTs are used extensively in fields like medicine,psychology,social sciences,and economics because they can provide strong evidence of causality.

Instrumental Variables is a statistical technique used in causal inference to address endogeneity or confounding when estimating causal relationships.Endogeneity can arise when there is a correltion between the independent variable and the error term in a regression model, often due to omitted variables, measurement errors, or reverse causality.

Mathing method aims to simulate the randomization process that occurs in RCTs by identifying units with similia characteristics, except for the treatment.By doing so, matching methods help to isolate the causal effect of the treatment on the outcome.

It's a statistical technique used to estimate causal relationships in obervational studies, particularly when randomization is not possible . DiD is commonly used to evaluate the impact of a policy change,treatment or intervention over time by comparing the differences in outcomes.

RDD is a quasi-experimental design used to estimate causal effects when randomization is not possible. It is particularly useful when an intervention or treatment is assigned based on a continuous running variable and there is a cutoff point or threshold that determines who receives the treatment , and uses this similarity to estimate causal effects.

SCM is a mathematical framework used to represent and analyze causal relationships between variables.It provides a formal way to encode assumptions about causality and allows for counterfactual reasoning , intervention analysis, and causal discovery.