Controlled experiments and causal inference

Here, we will be outlining how controlled experiments can enhance our understanding and determination of causal relationships. Moreover, we discuss advanced experimental designs beyond traditional A/B testing, such as multi-armed bandit tests and factorial designs, which offer sophisticated ways to explore and identify causality within complex systems. Understanding these concepts and methodologies is crucial for implementing causal inference in statistical programming environments such as R. Various R packages provide powerful tools for conducting causal inference analysis based on experimental data, allowing researchers and analysts to estimate causal effects, perform sensitivity analyses, and extend causal inference to observational studies.

Enhancing causal inference

Randomized controlled trials (RCTs) are the gold standard for establishing causal relationships by carefully manipulating one variable and measuring the impact on another...