"Unlocking Causal Insights with Field Experiments"

Causal inference in field experiments uses randomization and real-world data to identify cause-and-effect relationships while minimizing biases and confounding variables. This approach provides actionable insights with ecological validity, making findings applicable to real-world scenarios like policies and interventions.

Causal Inference in Context of Field Experiments

Causal inference is the process of determining whether one variable directly affects another. In the context of field experiments, it involves using real-world data and controlled settings to identify cause-and-effect relationships. Field experiments are conducted in natural environments where participants are exposed to interventions or treatments, and their outcomes are observed. By randomly assigning participants to treatment and control groups, field experiments minimize biases and confounding variables, which makes it possible to establish causal relationships. The randomization ensures that any differences in outcomes between the groups can be attributed to the intervention rather than external factors. Causal inference in field experiments is particularly valuable for understanding the impact of policies, programs, or interventions in practical, real-world scenarios. It provides actionable insights while maintaining ecological validity, which ensures that the findings are applicable to similar settings outside the experimental setup.

Infographic: The Quest for Causal Inference

The Quest for "Why?"

A visual guide to the foundations of causal inference—moving from simply seeing a pattern to proving a cause.

1. The Fundamental Problem

For any person, we can only observe one reality. The alternative—the **counterfactual**—is forever hidden.

🧑‍🔬

Person A

Gets Treatment 💊

Outcome is Observed

No Treatment 🚫

Outcome is Unobserved

2. The Goal: ATE

Since we can't see individual effects, we estimate the **Average Treatment Effect (ATE)** across a population.

E [Y(1)] - E [Y(0)]

Average Outcome (Treated) - Average Outcome (Control)

3. The Gold Standard: Randomization

Random Sampling

🌍

Select a representative group from the population.
(External Validity)

Random Assignment

⚖️

Split the sample into two identical-on-average groups.
(Internal Validity)

This ensures the only difference between groups is the treatment itself.

4. The Threat: Selection Bias

Without randomization, groups often form based on pre-existing traits, leading to biased results.

Population

🔵🔵⚪️⚪️🔵⚪️⚪️🔵

🔵 = Motivated, ⚪️ = Not Motivated

→

Treatment Group

🔵🔵🔵🔵

Mostly motivated people self-select.

Control Group

⚪️⚪️⚪️⚪️

Groups are not comparable.

5. The Rules: Core Assumptions for Valid Inference

SUTVA

No interference between units, and the treatment is consistent for all.

Ignorability

Treatment assignment is independent of potential outcomes (true by design in an RCT).

Positivity

For any group, there's a non-zero chance of receiving or not receiving the treatment.

Excludability

(For IVs) An instrument affects the outcome ONLY through the treatment.

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