POSTDICTIVE ANALYSIS: THE "RETROSPECTIVE BAYESIAN THEOREM" OF DATA ANALYSIS

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In the realm of data science, predictive analysis often grabs the spotlight, guiding businesses to anticipate future trends, customer behavior, and potential risks. However, there’s another equally powerful technique that deserves attention: postdictive analysis. While predictive analysis is about forecasting, postdictive analysis focuses on understanding and learning from the past. It provides a retrospective lens to validate, adjust, and refine our understanding of data, offering invaluable insights for continuous improvement.

What is Postdictive Analysis?

Postdictive analysis involves analyzing outcomes after they have occurred to understand the underlying factors that contributed to those results. It's akin to performing a post-mortem on past events to discern patterns, validate models, and refine strategies. This approach is critical in environments where understanding the "why" behind outcomes can lead to more informed decision-making and strategy refinement.

Why Postdictive Analysis Matters

1. Root Cause Identification: Postdictive analysis helps pinpoint the root causes of successes or failures by examining the factors that led to specific outcomes. This deep understanding enables organizations to replicate success and avoid repeating mistakes.
2. Refining Predictive Models: Postdictive analysis serves as a feedback loop for predictive models. By comparing predicted outcomes with actual results, data scientists can fine-tune their models, improving accuracy and reliability.
3. Mitigating Cognitive Biases: When predictions go wrong, it’s easy to overlook certain biases that might have influenced the model. Postdictive analysis helps uncover these biases by providing a reality check against real-world outcomes.
4. Continuous Improvement: By understanding past outcomes in detail, businesses can implement continuous improvements in processes, strategies, and decision-making frameworks.

How Postdictive Analysis is Conducted

1. Data Collection and Preprocessing

The first step in postdictive analysis is to gather and preprocess the relevant data. This involves collecting data on the events or outcomes of interest and cleaning it to ensure accuracy. Data preprocessing might include handling missing values, removing outliers, and normalising or standardising the data.

2. Descriptive and Exploratory Analysis

Next, perform descriptive statistics and exploratory data analysis (EDA) to understand the distributions, correlations, and trends in the data. Visualisation tools like histograms, scatter plots, and correlation matrices are crucial at this stage.

3. Model Validation and Adjustment

Once the data is understood, compare the outcomes predicted by your models with the actual outcomes. Calculate metrics like Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared to assess the accuracy of your models. If discrepancies are found, adjust the model parameters, feature selection, or even the model itself.

4. Root Cause Analysis

Delve into root cause analysis to identify the factors that most significantly influenced the outcomes. Techniques like decision trees, SHAP values, or LIME can be used to explain the predictions made by complex models.

5. Reporting and Continuous Improvement

Finally, compile the insights gained from the postdictive analysis into a report, highlighting the key findings, model adjustments, and actionable recommendations. Use these insights to drive continuous improvement in your processes and models.

Use Cases of Postdictive Analysis

1. Healthcare: Treatment Efficacy Evaluation
2. Finance: Fraud Detection
3. Retail: Sales Performance
4. Manufacturing: Quality Control

Challenges in Postdictive Analysis

1. Data Availability and Quality: Postdictive analysis relies on accurate and complete historical data. Inconsistent or missing data can lead to incorrect conclusions.
2. Overfitting: There’s a risk of overfitting models to past data, especially when tuning models based on postdictive analysis. This can lead to models that don’t generalise well to future data.
3. Cognitive Bias: Analysts might be biased by knowing the outcome, leading to confirmation bias in the analysis. It’s important to approach postdictive analysis with an objective mindset.

Conclusion

Postdictive analysis is a powerful tool that complements predictive analysis by offering a retrospective understanding of outcomes. By meticulously analysing past events, businesses can fine-tune their models, mitigate biases, and drive continuous improvement. As we increasingly rely on data-driven decision-making, postdictive analysis will play a crucial role in validating and refining the strategies that shape our future.

References

1. Altman, N. S., & Krzywinski, M. (2017). Points of Significance: Post hoc analysis. Nature Methods, 14(10), 931-933.
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4. Friedman, J., Hastie, T., & Tibshirani, R. (2001). The Elements of Statistical Learning. Springer.
5. Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). "Why Should I Trust You?" Explaining the Predictions of Any Classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1135-1144.
6. Lundberg, S. M., & Lee, S. I. (2017). A Unified Approach to Interpreting Model Predictions. Advances in Neural Information Processing Systems, 30, 4765-4774.
7. Ghorbani, A., Abid, A., & Zou, J. (2019). Interpretation of Neural Networks Is Fragile. Proceedings of the AAAI Conference on Artificial Intelligence, 33(1), 3681-3688.
8. Kolda, T. G., & Bader, B. W. (2009). Tensor Decompositions and Applications. SIAM Review, 51(3), 455-500.
9. Roscher, R., Bohn, B., Duarte, M. F., & Garcke, J. (2020). Explainable Machine Learning for Scientific Insights and Discoveries. IEEE Transactions on Neural Networks and Learning Systems, 32(11), 4263-4282.
10. Hand, D. J., & Yu, K. (2001). Idiot's Bayes—Not So Stupid After All? International Statistical Review, 69(3), 385-398.

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