Effect of treatment on the treated
The effect of treatment on the treated is the average causal difference between treated outcomes and their untreated potential outcomes among units that received treatment.
Causal Inference for Data Science
The average treatment effect on the treated is the average difference between treated outcomes and their untreated potential outcomes among treated units.
- How it is used
- Targets causal effects for units that actually received treatment when comparing treated outcomes with credible untreated counterfactuals.
- How deep it goes
- Defines ATT through potential outcomes and derives DiD estimation from observed data under the parallel paths assumption.
- Examples
- Uses overlapping patient propensity scores, chatbot engagement, movie releases, synthetic DiD data, and R and Python estimators.
- What you can do after
- Apply it via matching or difference-in-differences formulas, linear models, bootstrapping, and named R or Python libraries.
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Causal AI
The effect of treatment on the treated is the average causal effect among units that received treatment, comparing their treated outcomes with the outcomes they would have had under no treatment.
- How it is used
- Evaluates causal effects specifically among units exposed to treatment, especially when deployment assignment differs from experimental assignment.
- How deep it goes
- Derives binary ETT with counterfactual notation, consistency, SWIGs, d-separation, and probabilistic inference, while deferring broader nonbinary assumptions.
- Examples
- Uses recommendation-policy attrition and online-game engagement-and-purchases examples, including Pyro-style intervention and conditioning code.
- What you can do after
- Apply it via causal graphical models and probabilistic inference to estimate identified effects for treated populations.
Read how Causal AI explains effect of treatment on the treated →
Related concepts
- average treatment effect estimationkind of · Average treatment effect estimation quantifies the population-average difference in outcomes that would result from two alternative treatments or interventions.
- counterfactual outcomekind of · A counterfactual outcome is a hypothetical result that would have occurred under a condition, action, or intervention different from the one observed.
- treatment effectkind of · A treatment effect is the difference in an outcome attributable to receiving an intervention or exposure rather than a comparison condition.
- causal identificationaddressed by · Causal identification is the process of determining whether a causal quantity can be uniquely computed from available observational or interventional information and model assumptions.
- synthetic controlproduced by · Synthetic control is a causal inference method that estimates a treated unit’s counterfactual outcome using a weighted combination of comparison units.
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