The Analyst's Guide to Cause & Effect

Here are a few options, all aiming for a similar length and meaning: * **From "what" to "why": Causal inference for deeper insights.** * **Uncovering root causes: Applying causal inference techniques.** * **Going deeper: Analyzing "why" with causal methods.** * **Beyond events: Causal inference reveals underlying causes.** * **Shifting focus: Causal inference for true understanding.**

The Core Problem

A frequent pitfall is **confounding**, where an unobserved factor links two observed ones, leading to a false correlation.

Weather (Confounder)
Ice Cream Sales
Crime Rate
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The Gold Standard: RCTs

Randomization reigns supreme. By creating equivalent groups, it severs ties to hidden biases. This allows any outcome difference to be directly linked to the intervention.

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Population

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Treatment Group

Control Group

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Difference-in-Differences

Evaluates outcome shifts over time, contrasting treated and untreated groups. Requires the "parallel trends" assumption.

Regression Discontinuity

This method analyzes outcomes near a defined threshold, comparing those just on either side, assuming close similarity.

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More Tools

Instrumental Variables (IV)

Employs an "instrument" variable impacting treatment selection, yet not the outcome itself, to create a quasi-random assignment.

Propensity Score Matching (PSM)

Here are a few options, all similar in length: * Builds a control group by matching untreated individuals to treated ones with similar treatment propensities. * Forms a control group by finding untreated individuals similar to treated ones based on treatment likelihood. * Generates a control group by pairing untreated individuals with treated individuals, considering their propensity scores. * Creates a matched control group: untreated individuals are paired with treated individuals with a similar treatment chance.

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The Unspoken Rules

Analyzing cause from non-experiments hinges on unproven assumptions. These require validation via expert understanding.

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SUTVA

Here are a few options, all similar in length: * Units are independent, treatment is transparent. * Units unaffected, treatment fully visible. * No unit interaction, treatment is explicit. * Independent subjects, clear treatment delivery.

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Unconfoundedness

* All confounding variables are accounted for. * Confounders have been identified and addressed. * We've controlled for all relevant variables. * The analysis considers all key influences. * Potential biases are adequately managed.

Positivity

Everyone, regardless of their characteristics, has a chance of being assigned to either group.