IncomeSCM: From tabular data set to time-series simulator and causal estimation benchmark
IncomeSCM:从表格数据到时间序列模拟器与因果估计基准
Evaluating observational estimators of causal effects demands information that is rarely available: unconfounded interventions and outcomes from the population of interest, created either by randomization or adjustment. As a result, it is customary to fall back on simulators when creating benchmark tasks. Simulators offer great control but are often too simplistic to make challenging tasks, either because they are hand-designed and lack the nuances of real-world data, or…