Econ 599 Problem Set 3 Due April 27, 2018 Note For This Prob

Econ 599 Problem Set 3due April 27 2018note For This Problem Set Pl

Econ 599 Problem Set 3due April 27, 2018note For This Problem Set Pl

Analyze the Lalonde NSW data by performing multiple steps including data loading, summary statistics, propensity score estimation, trimming, stratification, and treatment effect estimation. Additionally, for the document classification, load the 20 newsgroups dataset, analyze its features via dimensionality reduction, train a supervised model to predict topics, tune hyperparameters, and evaluate performance.

Paper For Above instruction

Introduction

This paper addresses two primary tasks: the analysis of an econometric dataset (Lalonde NSW data) to estimate causal effects, and the classification of text documents from the 20 Newsgroups dataset to evaluate machine learning techniques. These tasks encompass various fundamental techniques in econometrics and machine learning, including data preprocessing, propensity score estimation, stratification, treatment effect estimation, text vectorization, dimensionality reduction, supervised classification, model tuning, and evaluation.

Part 1: Analysis of Lalonde NSW Data

Data Loading and Summary Statistics

The Lalonde dataset, drawn from observational studies evaluating labor interventions, contains information on earnings and covariates. Using the causalinference library in Python, the first step involves loading the dataset with the lalonde data method. Essential variables include earnings in 1978 as the outcome, and covariates such as race indicators, age, marital status, education, and unemployment status in 1974 and 1975. After loading, summary statistics can be generated using the CausalModel class, which provides descriptive analyses. Among the covariates, the normalized difference, calculated as the difference in means scaled by the pooled standard deviation, indicates the degree of imbalance. The covariate with the largest normalized difference signifies the greatest imbalance between treatment groups, which is critical for understanding confounding factors.

Propensity Score Estimation

Next, the propensity scores—probabilities of treatment assignment conditioned on covariates—are estimated using the est_propensity_s function. The selected covariates include variables such as earnings and unemployment statuses from the previous years. The selection algorithm considers linear terms and second-order (quadratic) terms, capturing nonlinearities and interactions. The algorithm outputs which additional terms, such as squared terms or interaction effects, were incorporated into the propensity score model, thus refining its accuracy.

Trimming of Extreme Propensity Scores

To improve the comparability of treatment groups, the sample is trimmed by removing observations with extreme propensity scores—values close to 0 or 1 that can cause estimations to be unstable. The cut-off point is determined by the trim_s function, which discards observations with scores outside the selected range. The exact cut-off and the number of observations dropped provide insight into the quality of the overlap and the representativeness of the sample.

Sample Stratification

The dataset is then stratified using the stratify_s function, which divides the sample into several propensity score bins or strata based on effort to achieve balance within each bin. The number of bins created depends on the specifications, and summary statistics within each bin are reported, including means and standard deviations of covariates, to assess the homogeneity of groups and the effectiveness of stratification in reducing bias.

Treatment Effect Estimation

Finally, the average treatment effect (ATE) is estimated using three methods: ordinary least squares (OLS), blocking (stratification), and matching. OLS estimates the effect controlling for covariates. Blocking estimates the effect within strata of propensity scores, minimizing bias due to confounding variables. Matchings, set at two matches per treated unit, include bias adjustment techniques to reduce estimation bias. Comparing these estimates reveals the robustness and sensitivity to the method choice; differences among these estimates highlight potential model dependence and residual bias.

Part 2: Document Classification

Data Loading and Inspection

The second task involves working with the 20 Newsgroups data available via sklearn.datasets. The dataset contains approximately 18,000 posts spanning 20 distinct topics. Loading it with fetch_20newsgroups provides access to both training and test data. Sample posts are printed to illustrate content diversity, and the list of topic names, which serve as class labels, is printed for reference.

Text Vectorization

To prepare data for machine learning models, the posts are transformed into numerical feature vectors using the bag-of-words model, which counts word occurrences. This process results in high-dimensional sparse vectors, with each dimension corresponding to a unique word in the dataset’s vocabulary. The dimensionality of these vectors is equal to the total number of unique words that appear across all posts, which is typically in the tens of thousands or more, reflecting the rich vocabulary used in news group posts.

Dimensionality Reduction

Given the high dimensionality, applying a dimensionality reduction technique—such as Principal Component Analysis (PCA)—compresses the vectors into a lower-dimensional space, with a specified number of components (K=30). This step preserves most of the variance while significantly reducing computational complexity, and helps mitigate overfitting in subsequent modeling.

Supervised Learning and Model Tuning

A supervised classifier, such as Logistic Regression or Support Vector Machine (SVM), is trained to predict the topic of each post based on the reduced features. Hyperparameters including the number of dimensions (K) are tuned using cross-validation on the test set. Accuracy scores are computed to evaluate model performance, with the goal of maximizing the classification accuracy. Hyperparameter tuning involves searching over different values for K, and selecting the model configuration with the highest validation accuracy.

Results and Conclusions

The highest accuracy achieved indicates the effectiveness of the feature extraction, dimensionality reduction, and modeling pipeline. Typically, there is a trade-off between the number of features retained and the classifier’s performance. Proper tuning results in a model that balances complexity and generalization ability, providing meaningful insights into text categorization.

Conclusion

This study demonstrates applications of econometric and machine learning techniques to real-world datasets. In the economic context, proper causal inference relies on bias reduction and appropriate estimations, including propensity score analysis and stratification. For text classification, effective data preprocessing, dimensionality reduction, and model tuning are key to achieving high predictive accuracy. Integrating rigorous statistical methodologies with machine learning practices offers powerful tools for analyzing complex data structures in economics and information science.

References

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