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Concept Graph & Resume using Claude 3.5 Sonnet | Chat GPT4o | Llama 3:
Resume:
1.- Randomized Control Trials (RCTs): Experimental method to test causal effects of interventions by randomly assigning treatments to groups.
2.- Machine Learning in Policy: Using ML techniques to analyze and improve policy experiments, especially in development economics.
3.- Plumbers vs Mechanics: Economists as "plumbers" experimenting with policies; ML experts as "mechanics" fine-tuning analytical tools.
4.- Double Machine Learning: Technique combining ML with econometric methods to estimate treatment effects while controlling for many variables.
5.- Causal Inference: Determining cause-and-effect relationships, a key goal in policy experiments and econometrics.
6.- Treatment Effect Heterogeneity: Variations in how different subgroups respond to interventions, important for tailoring policies.
7.- Multiple Treatments: Experimenting with various interventions simultaneously to find optimal policy combinations.
8.- Smart Pooling and Pruning: Method to efficiently analyze experiments with many treatment arms by grouping similar treatments.
9.- Pre-analysis Plans: Detailed plans specifying analyses before conducting experiments to prevent data mining and increase transparency.
10.- Incentives in Development: Using financial or other incentives to encourage desired behaviors, like immunization.
11.- Social Network Interventions: Leveraging community connections to spread information and influence behavior.
12.- SMS Reminders: Using text messages to remind people of important actions, like getting vaccinated.
13.- Conditional Average Treatment Effect (CATE): Estimating how treatment effects vary based on observable characteristics.
14.- Cost-effectiveness Analysis: Evaluating interventions based on their impact relative to their cost.
15.- Data Collection Challenges: Difficulties in obtaining high-quality data in development contexts, often requiring custom solutions.
16.- Adaptive Trials: Experiments that adjust treatments based on interim results to optimize outcomes.
17.- Explainability in ML: The challenge of making complex ML models understandable to policymakers and the public.
18.- Administrative Data: Utilizing existing government records for research, often requiring extensive cleaning and processing.
19.- Multiple Inference Problem: Statistical challenges when testing many hypotheses simultaneously, requiring adjustment of significance levels.
20.- Personalized Treatment Effects: Estimating how interventions affect individuals differently based on their characteristics.
21.- Immunization Promotion: Strategies to increase childhood vaccination rates in developing countries.
22.- Proxy Outcomes: Using easily measurable outcomes to predict or represent harder-to-measure impacts of interest.
23.- Sample Splitting: Technique to avoid overfitting by using separate data for model training and evaluation.
24.- Winner's Curse: Statistical phenomenon where the best-performing treatment in a study may appear more effective than it truly is.
25.- Interdisciplinary Collaboration: Importance of economists and ML experts working together to solve complex policy problems.
26.- Scaling Interventions: Challenges in expanding successful small-scale experiments to larger populations.
27.- Policy Relevance: Ensuring research directly informs and improves real-world policies and practices.
28.- Data Limitations: Working with smaller datasets common in development economics, unlike big data in commercial ML applications.
29.- Experimental Design: Carefully structuring studies to balance multiple research objectives and practical constraints.
30.- Impact Evaluation: Assessing the effects of development programs on people's lives, aiming to improve policy decisions.
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