Psychology of Causality - Causal Effects and the CounterFactual - Quantitative Analysis Series

Topic: Causality - Causal Effects and the CounterFactual

Experimental Research examines how a treatment causally affects an outcome by assigning varying values of the treatment variable to different observations and measuring their corresponding values of the outcome variable.

Causal Effects and the CounterFactual - Causal inference is a comparison between the factual (what actually happened) and the counterfactual (what would have happened if a key condition were different) (Imai & Bougher, 2021).

Observational Studies - Although RCTs can provide an internally valid estimate of causal effects, in many cases social scientists are unable to randomize treatment assignment in the real world for ethical and logistical reasons.

Confounding Bias - A pretreatment variable that is associated with both the treatment and the outcome variables is called a Confounder and is a source of Confounding Bias in the estimation of the treatment effect.

Quantiles - Quantiles divide a set of observations into groups based on the magnitude of the variable. An example of quantiles is the Median, which divides the data into two groups, one with a lower data value and the other with higher values (Imai & Bougher, 2021).

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