Pre-Estimation Checks

Important Links

• CCP Overview

• Quick Start

• Simulation Study

• Counterfactuals

Read this page before fitting CCP. These checks focus on support and conditioning because the estimator leans on first-stage choice probabilities.

Some CCP estimation risks are visible before optimization starts. Run these checks before treating a result as structural evidence.

Check	Why it matters
Feature rank	Collinear reward features cannot identify separate parameters.
Feature condition number	Near-collinearity inflates standard errors.
Transition row sums	Each transition row must be a valid probability distribution.
State coverage	Unvisited states require extrapolated CCPs.
State-action coverage	One-action states make counterfactual action values weakly supported.
Minimum positive CCP	Very small probabilities make the log correction unstable.
Reward normalization	Additive reward shifts do not change choice probabilities, and the shock scale must be normalized.
Transition orientation	CCP expects transition tensors in action, state, next-state order.

The direct action-contrast rank is a conservative check used by the wrapper. It is sufficient for the supported examples. It is not a general necessity. A state-only feature can affect dynamic choices when actions induce different future state distributions.

Support checks answer a different question from identification. Population full support permits the logit inversion. Observed zeros and thin cells make the empirical inverse unstable. Smoothing prevents a numerical zero, but it does not create information that was absent from the panel.

Simulation Checks

The first 20-state inference panel in the Simulation Study reports the following checks.

Check	Value	Status
Reward features	3	pass
Feature rank	3 / 3	pass
Action-contrast rank	3 / 3	pass
Feature condition number	1.000	pass
Action-contrast condition number	1.000	pass
Observed states	20 / 20	pass
State-action coverage	1.000	pass
Single-action states	0	pass
States with fewer than five observations	0	pass
Action counts	5,605 and 4,395	pass

The panel has full support, so the reported fit does not rely on uniform CCP fallbacks for unvisited states.

Common Risk Patterns

Data with many unvisited states force CCP to extrapolate the first-stage policy. States with only one observed action make counterfactual action values weakly supported. Too little smoothing can make log corrections unstable, while too much smoothing biases the empirical policy toward uniform choice. A transition tensor with the wrong orientation can have valid dimensions while representing the wrong transition law.

Before interpreting the estimates structurally, specify the discount factor, shock distribution and scale, reward location normalization, and utility form. The wrapper can estimate transitions from the panel, but its reward standard errors do not propagate uncertainty from that transition stage.