Making the Most out of Programme Evaluations and Social Experiments: Accounting for Heterogeneity in Programme Impacts

By James J. Heckman, Jeffrey A. Smith, N. Clements. 1997

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Citations in the corpus

Metalearners for estimating heterogeneous treatment effects using machine learning (2017, background): “where [32] follows from Jensen’s inequality.”
Identifying distributional characteristics in random coefficients panel data models (2009, methodology): “The following estimator of the unconditional variance matrix of errors, based on (23), uses covariance restrictions in levels:”
The changes-in-changes model with covariates (2015, background): “(6) 6See the interesting discussion in Heckman, Smith, and Clements (1997).”
Moving to Opportunity and Tranquility: Neighborhood Effects on Adult Economic Self-Sufficiency and Health From a Randomized Housing Voucher Experiment (2004, result): “Under an assumption of perfect rank correlation between treatment and control complier outcomes (Heckman, Smith, and Clements 1997), these results are consistent with significant reductions in distress within the more- distressed half of the complier population.”
Quantile Selection Models with an Application to Understanding Changes in Wage Inequality (2017, unknown): “[38] Heckman, J.”
SHARP BOUNDS ON THE VARIANCE IN RANDOMIZED EXPERIMENTS (2014, background): “See Copas [2], Gadbury, Iyer and Albert [6], Heckman, Smith and Clements [9] and Zhang et al.”
PARTIAL IDENTIFICATION OF THE DISTRIBUTION OF TREATMENT EFFECTS AND ITS CONFIDENCE SETS (2009, methodology): “…on the dependence structure between the potential outcomes such that distributions of the treatment effects are point identified, see, for example, Heckman et al. (1997), Biddle, Boden, and Reville (2003), Carneiro, Hansen, and Heckman (2003), Aakvik, Heckman, and Vytlacil (2005), and Abbring and…”
Inference on counterfactual distributions (2009, unknown): “…≤ δ } dudFX0(x), j, k ∈ {0, 1}, (2.6) where Q∆0(u|x) = QY0(u|g(x)) −QY0(u|x) and Q∆1(u|x) = QY1(u|x) −QY0(u|x); and Qk∆j (u) = inf{δ : F k ∆j (δ) ≥ u}, j, k ∈ {0, 1}, (2.7) under the additional assumption (Heckman, Smith, and Clements, 1997): Condition RP. Conditional rank preservation: U10 = U…”
SHARP BOUNDS ON THE DISTRIBUTION OF TREATMENT EFFECTS AND THEIR STATISTICAL INFERENCE (2009, methodology): “Recently,[ F−11 (q)− F−10 (q) ] has been used to study treatment effects heterogeneity and is referred to as the QTE; see, e.g., Heckman et al. (1997), Abadie et al. (2002), Chernozhukov and Hansen (2005), Firpo (2007), and Imbens and Newey (2005), among others, for more discussion and references…”
The Employment Effects of Job Creation Schemes in Germany: A Microeconometric Evaluation (2008, unknown): “In line with this finding are the results of Hübler (1997), who states that the programmes do not achieve the expected positive impacts.”
Evaluation in the Practice of Development (2008, unknown): “We may also want to estimate the joint distribution of outcomes under treatment and outcomes under the counterfactual, and a method for doing so is outlined in Heckman et al. (1997).”