By Learn Statistics Easily
Controlled experiments clarify cause-and-effect by manipulating one variable and observing its impact.
Conditions mimicking controlled experiments in the natural world can illuminate causal links.
Observing variables over time reveals how changes in one can influence another.
If one time series can predict another, a causal relationship might be present.
Using an instrument unrelated to confounders can help estimate true causal effects.
Examining data around cutoffs can reveal the true impact of interventions.
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Combining multiple studies clarifies the overall strength and consistency of causal effects.
Aligning groups based on similar characteristics can highlight real causal impacts.
Assessing how outcomes evolve over time between groups can pinpoint causal effects.
Identifying intermediary variables that transmit the effects from cause to outcome.
The strength of causal relationships can shift based on different external conditions.
Considering what might have happened under different circumstances helps estimate causal effects.
Accounting for unseen, constant influences helps isolate the effect of interest.
Embrace a deeper dive into causation by exploring the detailed article.