Thuật ngữ AI
Từ điển đầy đủ về Trí tuệ nhân tạo
Difference-in-Differences (DiD)
A quasi-experimental method that estimates the causal effect of a treatment by comparing the change in the outcome over time for a treatment group to a control group, before and after the intervention.
Regression Discontinuity Design (RDD)
A causal inference approach that exploits a threshold or cutoff in treatment assignment to compare units just on either side of this discontinuity, who are assumed to be similar except for the treatment.
Causal Quantile Treatment Effect (QTE)
A measure of the heterogeneous effect of a treatment at different quantiles of the outcome distribution, offering a more nuanced view than the average effect by revealing how the impact varies across the distribution.
Heterogeneity of Causal Effects (HTE)
The study of how the causal effect of a treatment varies across subpopulations or based on individual characteristics, crucial for personalization and algorithmic fairness.
Causal Matching
A technique that aims to create comparable treatment and control samples by matching each treated unit with one or more untreated units that have similar covariate values, often using the propensity score.
Latent Variable Model for Causality
A statistical approach that incorporates unobserved (latent) variables to represent confounding factors or hidden traits, allowing for the modeling of more complex and realistic causal relationships.