Program Evaluation as a Decision Problem *

By Rajeev Dehejia. 2002

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The Econometrics of Randomized Experiments (2016, methodology): “Let us illustrate this using data from National Supported Work program, previously analyzed by Lalonde (1986), Dehejia and Wahba (1999) and many others.”
Estimation and Inference of Heterogeneous Treatment Effects using Random Forests (2015, methodology): “We also show that the heuristically motivated infinitesimal jackknife for random forests developed by Efron [2014] and Wager et al. [2014] is consistent for the asymptotic variance of random forests in this setting. Our proof builds on classical ideas from Efron and Stein [1981], Hájek [1968], and H”
Efficient Policy Learning (2017, methodology): “Between 1988 and 1993, the Manpower Development Research Corporation conducted a randomized study to evaluate the program. As described in Hotz, Imbens, and Klerman (2006), randomly chosen registrants were eligible to receive GAIN benefits immediately, whereas others were embargoed from the program ”
Impossibility Results for Nondifferentiable Functionals (2012, background): “To see how lack of smoothness in κ̇ affects the possibility of unbiased or equivariant estimation, we partition Z into its first component and the remaining subvector, with parameters partitioned comformably: Z = ( Z1 Z2 ) ∼ N (( h1 h2 ) , ( σ11 Σ1,2 Σ2,1 Σ2,2 )) .”
Inferring Welfare Maximizing Treatment Assignment Under Budget Constraints (2012, methodology): “It has the obvious advantage of being robust to distributional misspecifications and is therefore our preferred method.”
Complementarity and Aggregate Implications of Assortative Matching: A Nonparametric Analysis (2014, unknown): “…evaluation literature.3 More directly, it is complementary to the small literature on the effect of treatment assignment rules (Manski (2004), Dehejia (2005), Hirano and Porter (2009), 3For recent surveys, see Angrist and Krueger (1999), Heckman, Lalonde, and Smith (2000), and Imbens and…”
Using Experiments to Correct for Selection in Observational Studies (2020, unknown): “…include simple average treatment effects, but more generally also the average effect of a policy that assigns the treatment to individuals in this population on the basis of covariates ( e.g. , Manski [2004], Dehejia [2005], Hirano and Porter [2009], Athey and Wager [2017], Zhou et al. [2018]).”
Asymptotics for Statistical Treatment Rules (2009, background): “(4)For a discussion of the relationship between the net social welfare and traditional measures of effects of treatments, such as the average treatment effect, see Dehejia (2003).”
Estimation of Heterogeneous Treatment Effects from Randomized Experiments, with Application to the Optimal Planning of the Get-Out-the-Vote Campaign (2011, methodology): “This framework is based on the recently developed literature about treatment choice (e.g., Manski 2005), but it differs from this literature by adopting a Bayesian approach rather than a frequentist approach based on the maximin or minimax regret criteria (see Dehejia 2005, for an exception).”
Statistical treatment rules for heterogeneous populations (2003, unknown): “To the extent that statistical decision theory has been applied to treatment choice, it has been the conditional Bayes paradigm rather than that of Wald; see, for example, Rubin (1978) and Dehejia (2003). The remainder of this paper applies Wald’s statistical decision theory to the problem of treatm”
Modeling heterogeneous treatment effects in large-scale experiments using Bayesian Additive Regression Trees (2010, background): “…et al. 2002; Royston and Sauerbrei 2004; Rothwell 2005) and econometrics (e.g., Heckman, Smith, and Clements 1997; Abadie 2003; Poirier and Tobias 2003; Angrist 2004; Dehejia 2005; Bitler, Gelbach, and Hoynes 2006, 2009; Abbring and Heckman 2007; Crump et al. 2008; Djeb- 1 bari and Smith 2008).”
The Connection Between Varying Treatment Effects and the Crisis of Unreplicable Research (2015, background): “In policy analysis, this implies different effects for different people under different scenarios (Dehejia, 2005), and it connects to goals such as personalized medicine (or, less pleasantly, individuallytargeted marketing pitches).”
BAYESIAN ASPECTS OF TREATMENT CHOICE (2011, methodology): “CONNECTIONS WITH THE LITERATURE Dehejia (2005) applies Bayesian decision theory to program evaluation.”
Inferring Optimal Peer Assignment From Experimental Data (2009, background): “The article is closely related to the treatment choice literature, c.f. Manski (2004), Dehejia (2005), and Hirano and Porter (2005)—in that it concerns designing optimal policies based on results of a random experiment.”
Evaluating the Foreign Ownership Wage Premium Using a Difference-in-Differences Matching Approach (2006, methodology): “The next step in the analysis is to check the sensitivity of the results by making a slight change to the propensity score specification suggested by Dehejia (2005).”