The article discusses how omitting important factors can bias the estimation of how certain variables affect outcomes. The researchers introduce a method called Experimentally Randomized Instrumental Variables (ERIVs) to correct this bias. They show that many studies incorrectly interpret their results as causal when they are affected by bias. By using ERIVs in experiments, researchers can accurately estimate the causal effects of measured variables on outcomes.