the back-door criterion identifies: sufficient adjustment sets for causal estimation

Causal model

Causal models use DAGs to represent causal relationships

an instrumental variable does: isolates causal effect when you can't randomize

Instrumental variables isolate causal effects in non-randomized studies

Chebyshev's inequality

Chebyshev's inequality limits the probability of deviation from the mean

classifier-free guidance does: interpolates between conditional and unconditional generation

"Classifies samples as either conditioned or unconditioned, guiding generation towards desired outcomes."

Regression discontinuity design

RDD uses a sharp threshold for treatment assignment

Metropolis–Hastings algorithm

Metropolis-Hastings algorithm samples from difficult distributions