Identification of Unobservables in Observations
In empirical studies, the data usually don't include all the variables of interest in an economic model. This paper shows the identification of unobserved variables in observations at the population level. When the observables are distinct in each observation, there exists a function mapping from the observables to the unobservables. Such a function guarantees the uniqueness of the latent value in each observation. The key lies in the identification of the joint distribution of observables and unobservables from the distribution of observables. The joint distribution of observables and unobservables then reveal the latent value in each observation. Three examples of this result are discussed.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Heterogeneity, Uncertainty and Learning: Semiparametric Identification and Estimation
We provide identification results for a broad class of learning models in which continuous outcomes depend on three types of unobservables: known heterogeneity, initially unknown heterogeneity that may be revealed over t…
Controlling for Latent Confounding with Triple Proxies
We present new results for nonparametric identification of causal effects using noisy proxies for unobserved confounders. Our approach builds on the results of \citet{Hu2008} who tackle the problem of general measurement…
Toward Supporting Perceptual Complementarity in Human-AI Collaboration via Reflection on Unobservables
In many real world contexts, successful human-AI collaboration requires humans to productively integrate complementary sources of information into AI-informed decisions. However, in practice human decision-makers often l…
Choosing Exogeneity Assumptions in Potential Outcome Models
There are many kinds of exogeneity assumptions. How should researchers choose among them? When exogeneity is imposed on an unobservable like a potential outcome, we argue that the form of exogeneity should be chosen base…
Assessing Omitted Variable Bias when the Controls are Endogenous
Omitted variables are one of the most important threats to the identification of causal effects. Several widely used methods assess the impact of omitted variables on empirical conclusions by comparing measures of select…
Sensitivity