A Guide to Impact Evaluation under Sample Selection and Missing Data: Teacher's Aides and Adolescent Mental Health
This paper is concerned with identification, estimation, and specification testing in causal evaluation problems when data is selective and/or missing. We leverage recent advances in the literature on graphical methods to provide a unifying framework for guiding empirical practice. The approach integrates and connects to prominent identification and testing strategies in the literature on missing data, causal machine learning, panel data analysis, and more. We demonstrate its utility in the context of identification and specification testing in sample selection models and field experiments with attrition. We provide a novel analysis of a large-scale cluster-randomized controlled teacher's aide trial in Danish schools at grade 6. Even with detailed administrative data, the handling of missing data crucially affects broader conclusions about effects on mental health. Results suggest that teaching assistants provide an effective way of improving internalizing behavior for large parts of the student population.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Unified View Imputation and Feature Selection Learning for Incomplete Multi-view Data
Although multi-view unsupervised feature selection (MUFS) is an effective technology for reducing dimensionality in machine learning, existing methods cannot directly deal with incomplete multi-view data where some sampl…
feature selectionImputationModel Performance-Guided Evaluation Data Selection for Effective Prompt Optimization
Optimizing Large Language Model (LLM) performance requires well-crafted prompts, but manual prompt engineering is labor-intensive and often ineffective. Automated prompt optimization techniques address this challenge but…
BenchmarkingClusteringLarge Language ModelPrompt EngineeringImpacts of Dirty Data: and Experimental Evaluation
Data quality issues have attracted widespread attention due to the negative impacts of dirty data on data mining and machine learning results. The relationship between data quality and the accuracy of results could be ap…
BIG-bench Machine LearningClusteringGeneral Classification$Δ$-AttnMask: Attention-Guided Masked Hidden States for Efficient Data Selection and Augmentation
Visual Instruction Finetuning (VIF) is pivotal for post-training Vision-Language Models (VLMs). Unlike unimodal instruction finetuning in plain-text large language models, which mainly requires instruction datasets to en…
The Responsible Foundation Model Development Cheatsheet: A Review of Tools & Resources
Foundation model development attracts a rapidly expanding body of contributors, scientists, and applications. To help shape responsible development practices, we introduce the Foundation Model Development Cheatsheet: a g…