A/B Tests – The Gold Standard of Causal Inference

Why randomization eliminates all confounding—both observed and unobserved.

Perfect when you fully control assignment

Why A/B tests are considered the gold standard

A/B tests (randomized controlled trials) are the most reliable way to measure causal impact because randomization eliminates systematic differences between treated and control groups. No other method guarantees the removal of all confounders—known or unknown.

How randomization removes all confounders

In observational data, treatment assignment is influenced by many factors. Users self-select into behaviors, features, campaigns, or policies. These choices correlate with outcomes and create confounding.

Randomization destroys these correlations completely.

1. Independence

Treatment T is assigned independently of potential outcomes Y(0), Y(1). Formally:

(Y(0), Y(1)) ⟂ T

2. Balance

With sufficient sample size, treated and control groups have the same distribution of:

demographics
past behavior
engagement
preferences
device, geography, time effects
any unobserved latent traits

3. Exchangeability

Any treated user could have easily been a control user. This symmetry ensures differences in outcomes reflect only the treatment.

The A/B test estimator

ATE = mean(Y | T=1) − mean(Y | T=0)

This simple difference is unbiased because randomization ensures both groups are identical in expectation.

Why A/B tests outperform observational methods

No parallel trends assumption (unlike DiD)
No propensity model or overlap issues (unlike PSM)
No donor-weighting assumptions (unlike Synthetic Control)
No model dependence or specification sensitivity
No hidden confounders—randomization eliminates them

When A/B tests are not feasible

A/B tests break down when:

You can't randomize (e.g., pricing, policy, legal constraints)
Treatment happens at country/geo level with few units
There are strong spillovers or interference
There are long-term network effects

A/B tests inside

integrates A/B testing alongside sophisticated observational methods, allowing data scientists to:

Run classic randomized experiments
Run holdouts and cluster experiments
Validate exposure, compliance, and sample balance
Compare A/B results to DiD, PSM, or Synthetic Control on the same dataset