Causal Inference Methods - Estimating Causal Effects: Investigating causal inference methods for estimating causal effects from observational data to make informed decisions
Keywords:
Causal Inference, Causal EffectsAbstract
Causal inference methods play a crucial role in estimating causal effects from observational data, providing valuable insights for decision-making in various fields. This paper reviews and analyzes popular causal inference methods, including propensity score matching, instrumental variables, and regression discontinuity, highlighting their strengths, limitations, and real-world applications. Additionally, we discuss challenges in causal inference, such as unmeasured confounding and selection bias, and propose strategies to address these challenges. By understanding and utilizing these methods effectively, researchers and practitioners can make informed decisions based on causal relationships inferred from observational data.
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