Accelerator Selection Bias Correction

Accelerator Selection Bias Correction MCP Connector for Claude

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Quantify and correct selection bias in accelerator program outcomes.

3 tools Official Updated Oct 1, 2026 Official Vinkius Partner

This MCP server provides analytical tools to identify and correct selection bias in accelerator programs. By comparing the performance of accepted companies against rejected applicants, it helps program managers understand the true impact of their selection process. Use calculate_bias_magnitude to find the statistical gap between cohorts, estimate_true_value_add to isolate the program's actual impact from pre-existing excellence, and evaluate_selection_effectiveness to measure how well selection criteria predict future success.

acceleratorselection-biasdata-analysisstartup-metricspredictive-modeling

3 tools expose this connector's capabilities to your AI agent.

calculate_bias_magnitude

Calculate the magnitude of selection bias

estimate_true_value_add

Estimate the true value-add of the accelerator

evaluate_selection_effectiveness

Evaluate how well selection criteria predict success

See how to talk to your AI agent using Accelerator Selection Bias Correction.

How much selection bias is present in our current cohort?

The selection bias magnitude is 0.15, indicating a significant gap between the accepted and rejected groups.

What was the true value-add of the accelerator after adjusting for bias?

The estimated true value-add is 25% uplift over the baseline success rate.

How accurate are our selection criteria at predicting success?

The selection model accuracy is 0.72, suggesting a strong predictive power for future outcomes.

Selection bias occurs when the cohort of accepted companies is not a random sample, meaning they may have inherent qualities that lead to success regardless of the program's influence.

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