Large Signal ModelReading edition

75. Actions need coverage, not only more observations

Reading tools
Reading libraryFind statementsInspect Lean
Switch work

Saved reading

Read side by side

Start reading

Resources & source

Original TeX with line links · Download TeX

The wording and mathematics follow the retained manuscript. This reading format does not add a proof review or extend Lean coverage.

Skip contents and start reading

75. Actions need coverage, not only more observations

For intervention evidence use the information \(\mathcal H_{t-1}\) just before choosing the action. Let the action be randomized from a known history-dependent policy \(\pi_t\). The true next-observation kernel under action \(a\) is \(P_t(\cdot\mid\mathcal H_{t-1},a)\). Randomization uses no unrecorded future information. For a predictable test \(\phi_t\in[0,1]\), define

\[ \mu_t^a=\int\phi_t(y)P_t(dy\mid\mathcal H_{t-1},a),\qquad W_t^a=\frac{\mathbf1_{\{A_t=a\}}\phi_t(Y_t)}{\pi_t(a\mid\mathcal H_{t-1})}. \]

Proposition 67: Covered adaptive-action evidence

If the target action has \(\pi_t(a\mid\mathcal H_{t-1})\geq\pi_{\min}>0\) at every time, then

\[ \mathbb E[W_t^a\mid\mathcal H_{t-1}]=\mu_t^a, \qquad 0\leq W_t^a\leq1/\pi_{\min}. \]

Consequently Proposition 66 gives a time-uniform certificate for \(\sum_{s\leq t}\mu_s^a\) from \(\sum_{s\leq t}W_s^a\), with its boundary multiplied by \(1/\pi_{\min}\). The target is the average action-conditioned quantity along the encountered contexts, not a reset-population table or the state distribution under a different long-term policy.

Proof

Condition first on the action and then on its observation. Only action \(a\) contributes, with probability \(\pi_t(a\mid\mathcal H_{t-1})\), cancelling the denominator. The range bound follows directly. Apply the scaled conditional moment-generating-function argument to the resulting martingale differences. \(\square\)

This simple bound is conservative. Variance-aware weighting can improve it, but cannot remove the need for information about the action of interest. When \(\pi_{\min}=0\), two models can give exactly the same law under the observed policy and disagree on a never-selected action. For example, both can emit a Bernoulli observation with parameter \(1/2\) under action zero, while their action-one parameters are \(1/10\) and \(9/10\). Repeating action zero cannot distinguish them.

The action randomization and its recorded probabilities are part of the experimental assumption. An observational association with a hidden selection mechanism is not automatically an intervention estimate. Long-horizon off-policy predictions require additional occupancy or trajectory-coverage arguments that this one-step statement does not provide.

Source and conversion record

Source SHA-256: 0173ab16e349b8778dc66f60f59820f40f800cff75e2223de07dcc1a84af18ff

Unsupported prose commands stop the build. The mathematical expression sequence is checked against the source, with manuscript macros expanded for display.

Conversion records

Notation in Large Signal Model

Look up a symbol without leaving this chapter. Select text before opening help to search for it.

Search this work’s notation.

    Symbols can have different meanings in different passages. Read each result in its own context.

    Open the full notation index