"We suggest two nonparametric approaches, based on kernel methods and orthogonal series to estimating regression functions in the presence of instrumental variables. For the first time in this class of problems, we derive optimal convergence rates, and show that they are attained by particular estimators. In the presence of instrumental variables the relation that identifies the regression function also defines an ill-posed inverse problem, the ``difficulty'' of which depends on eigenvalues of a certain integral operator which is determined by the joint density of endogenous and instrumental variables. We delineate the role played by problem difficulty in determining both the optimal convergence rate and the appropriate choice of smoothing parameter."

"Conditional independence relations involving latent variables do not necessarily imply observable independences. They may imply inequality constraints on observable parameters and causal bounds, which can be used for falsification and identification. The literature on computing such constraints often involve a deterministic underlying data generating process in a counterfactual framework. If an analyst is ignorant of the nature of the underlying mechanisms then they may wish to use a model which allows the underlying mechanisms to be probabilistic. A method of computation for a weaker model without any determinism is given here and demonstrated for the instrumental variable model, though applicable to other models. The approach is based on the analysis of mappings with convex polytopes in a decision theoretic framework and can be implemented in readily available polyhedral computation software. Well known constraints and bounds are replicated in a probabilistic model and novel ones are computed for instrumental variable models without non-deterministic versions of the randomization, exclusion restriction and monotonicity assumptions respectively."

(From a quick scan, this looks too heavy to actually teach in ADAfaEPoV, but it's so tagged to remind me to include a reference.)

"Starting with Miguel, Satyanath, and Sergenti (2004), a large literature has used rainfall variation as an instrument to study the impacts of income shocks on civil war and conáict. These studies argue that in agriculturally-dependent regions, negative rain shocks lower income levels, which in turn incites violence. This identiÖcation strategy relies on the assumption that rainfall shocks a§ect conáict only through their impacts on income. I evaluate this exclusion restriction by identifying districts that are downstream from dams in India. In downstream districts, income is much less sensitive to rainfall áuctuations. However, rain shocks remain equally strong predictors of riot incidence in these districts. These results suggest that rainfall a§ects rioting through a channel other than income and cast doubt on the conclusion that income shocks incite riots."

Cute.

"Instrumental variable (IV) methods are widely used to identify causal effects in models with endogenous explanatory variables. Often the instrument exclusion restriction that underlies the validity of the usual IV inference is suspect; that is, instruments are only plausibly exogenous. We present practical methods for performing inference while relaxing the exclusion restriction. We illustrate the approaches with empirical examples that examine the effect of 401(k) participation on asset accumulation, price elasticity of demand for margarine, and returns to schooling. We find that inference is informative even with a substantial relaxation of the exclusion restriction in two of the three cases."

"We propose methods for inference on the average effect of a treatment on a scalar outcome in the presence of very many controls. Our setting is a partially linear regression model containing the treatment/policy variable and a large number $p$ of controls or series terms, with $p$ that is possibly much larger than the sample size $n$, but where only $s < n$ unknown controls or series terms are needed to approximate the regression function accurately. The latter sparsity condition makes it possible to estimate the entire regression function as well as the average treatment effect by selecting an approximately the right set of controls using Lasso and related methods. We develop estimation and inference methods for the average treatment effect in this setting, proposing a novel "post double selection" method that provides attractive inferential and estimation properties. In our analysis, in order to cover realistic applications, we expressly allow for imperfect selection of the controls and account for the impact of selection errors on estimation and inference. In order to cover typical applications in economics, we employ the selection methods designed to deal with non-Gaussian and heteroscedastic disturbances. We illustrate the use of new methods with numerical simulations and an application to the effect of abortion on crime rates."

"This article is about estimation and inference methods for high dimensional sparse (HDS) regression models in econometrics. High dimensional sparse models arise in situations where many regressors (or series terms) are available and the regression function is well-approximated by a parsimonious, yet unknown set of regressors. The latter condition makes it possible to estimate the entire regression function effectively by searching for approximately the right set of regressors. We discuss methods for identifying this set of regressors and estimating their coefficients based on $ell_1$-penalization and describe key theoretical results. In order to capture realistic practical situations, we expressly allow for imperfect selection of regressors and study the impact of this imperfect selection on estimation and inference results. We focus the main part of the article on the use of HDS models and methods in the instrumental variables model and the partially linear model. We present a set of novel inference results for these models and illustrate their use with applications to returns to schooling and growth regression."

"Measurement errors in economic data are pervasive and nontrivial in size. The presence of measurement errors causes biased and inconsistent parameter estimates and leads to erroneous conclusions to various degrees in economic analysis. While linear errors-in-variables models are usually handled with well-known instrumental variable methods, this article provides an overview of recent research papers that derive estimation methods that provide consistent estimates for nonlinear models with measurement errors. We review models with both classical and nonclassical measurement errors, and with misclassification of discrete variables. For each of the methods surveyed, we describe the key ideas for identification and estimation, and discuss its application whenever it is currently available." (Not read, reconsider to_teach tag later.)

"There is currently much debate about the effectiveness of foreign aid and about what kind of projects can engender economic development. There is skepticism about the ability of econometric analysis to resolve these issues or of development agencies to learn from their own experience. In response, there is increasing use in development economics of randomized controlled trials (RCTs) to accumulate credible knowl- edge of what works, without overreliance on questionable theory or statistical meth- ods. When RCTs are not possible, the proponents of these methods advocate quasi- randomization through instrumental variable (IV) techniques or natural experiments. I argue that many of these applications are unlikely to recover quantities that are use- ful for policy or understanding: two key issues are the misunderstanding of exogeneity and the handling of heterogeneity. I illustrate from the literature on aid and growth. Actual randomization faces similar problems as does quasi-randomization, notwith- standing rhetoric to the contrary. I argue that experiments have no special ability to produce more credible knowledge than other methods, and that actual experiments are frequently subject to practical problems that undermine any claims to statisti- cal or epistemic superiority. I illustrate using prominent experiments in development and elsewhere. As with IV methods, RCT-based evaluation of projects, without guid- ance from an understanding of underlying mechanisms, is unlikely to lead to scientific progress in the understanding of economic development. I welcome recent trends in development experimentation away from the evaluation of projects and toward the evaluation of theoretical mechanisms."

"Social scientists increasingly exploit natural experiments in their research. This article surveys recent applications in political science, with the goal of illustrating the inferential advantages provided by this research design. When treat- ment assignment is less than “as if” random, studies may be something less than natural experiments, and familiar threats to valid causal inference in observational settings can arise. The author proposes a continuum of plausibility for natural experiments, defined by the extent to which treatment assignment is plausibly “as if” random, and locates several leading studies along this continuum."

Shorter: I am sickened by the weakness of your instruments.

" people who like matching methods ... tend to believe that most confounders can be measured ... and that there aren't a lot of lurking unobservables. ... [P]eople ... who are skeptical of matching ... argue that there will always be problematic unobservables lurking ... [and they] prefer instrumental variables approaches .... [T]he same people who tell me that lurking unobservables are everywhere tend to be fairly comfortable making the ... exclusion restrictions that make IV approaches work. The crazy thing is that just like matching, these assumptions [are] about unobservable causal pathways. The claim that an instrumental variable is valid is the claim that there are no unobserved (or observed) variables linking the instrument to the outcome except through the path of the instrumented variable. ... [P]eople who think that lurking unobservables are everywhere in matching somehow think that all these lurking uobservables go away as soon as you call something an instrument..."

Those would be _instrumental_ variables. Implications for the collected scholarly works of S. Levitt left as an exercise for the reader.

Looks cool, though it depends on finding instrumental variables, which I always find sketchy.

Unfortunately, that NBER paper I linked to appears to be serious.

This is a joke, right? Right? Somebody please tell me this is a joke...

ETA: It's not a joke. It's now a negative example in ADA.

Ungated version: http://forum.johnson.cornell.edu/faculty/waldman/autism-waldman-nicholson-adilov.pdf