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In antitrust cases, counsel must first map the expert’s analytic framework to accessible terms, identifying core assumptions that drive conclusions. Begin by requesting the data sources and constructing a transparent timeline of the model’s inputs, outputs, and calibrations. Acknowledge legitimate uncertainty while pinpointing where small misalignments can cascade into large estimation errors. By laying out the model’s logic in plain language, you create opportunities to challenge attribution, causality, and market definition. The objective is not merely to contradict numbers, but to reveal how method choices shape outcomes and whether those choices align with established economic doctrine.

When cross examining, space is precious, so questions should be concrete and progressive rather than theoretical. Start with simple, checkable propositions: “Is the data representative of the relevant market?” or “Were all relevant products considered?” Each answer should be met with a pinpoint follow-up that forces the expert to disclose contingencies, data limitations, and any exclusions. Throughout, avoid grandiloquence and keep tempo steady. Use hypotheticals that mirror the real world but test the model’s resilience to variation. The aim is to create intellectual friction that exposes fragility without appearing antagonistic, thereby maintaining an honorable courtroom posture.

A strong strategy involves exposing sensitivity and robustness gaps in the model. Engage the expert with a sequence of scenarios that stress test coefficient stability, elasticities, and demand shifts. Ask for replication with alternative data sets and different time frames, then compare results. If the model reacts inconsistently, press for a transparent account of why certain inputs were prioritized over others. The corroboration burden should fall on the model’s assumptions, not on the counsel to memorize every technical nuance. By forcing explicit justification for each step, you illuminate why some results are unreliable in contested markets.

Do not rely on objections about “statistical complexity” as a shield. Translate jargon into practical implications by tying each methodological choice to decision relevance—price effects, consumer welfare, or merger efficiency claims. Demand a clear articulation of the counterfactual, the baseline market conditions, and the mechanism of effect. If the expert asserts precision, demand a confidence interval and a discussion of potential biases. A well-timed interruption to request a definition can stall overconfident assertions and reveal conceptual gaps, especially when the model’s external validity is uncertain.

The cross examiner should compile a concise glossary of technical terms used by the expert, then translate each term into plain-language equivalents. This creates a shared ground for collaboration with the judge and jury, while also enabling precise cross questioning. As you proceed, track every assumption that affects the ultimate conclusion—range of market participants, product substitutability, and observable market behavior. Document the expert’s reliance on any untestable premises. The aim is to constrain speculation and insist on verifiable links between input choices and claimed effects, ensuring the defense does not accept unsupported extrapolations.

A key tactic is to spotlight data quality and selection bias. Question how missing data, nonresponse, or timing misalignment might distort outcomes. Request the exact dataset size, the treatment of outliers, and the rationale for excluding certain observations. Probe whether alternative data streams yield similar results or reveal divergent implications. When the expert cites precision, press for sensitivity analyses and out-of-sample validation. The stronger the implausibility of stable results across reasonable variations, the more credible the critique becomes. This approach anchors the cross in empirical reality, not abstract theory alone.

Next, scrutinize model specification and the transparency of computations. Encourage the expert to disclose the full code, formulas, and parameter estimation steps. If documentation is missing or incomplete, highlight how unreproducible results undermine reliability. Ask for a step-by-step walkthrough showing how each input impacts the final verdict. When the model depends on proprietary or undisclosed algorithms, insist on a legally acceptable explanation and, if possible, a third-party verification. A credible challenge to opacity strengthens your leverage to question the model’s legitimacy in the antitrust context.

Build a narrative that connects technical decisions to practical consequences. Tie estimation choices to real-world effects on consumer prices, access to essential goods, or market competition. Use clear examples illustrating how a single assumption—such as constant margins—could artificially amplify effects in a way that misleads the court. The jury benefits from a steady stream of concrete implications rather than abstract numerics. Your cross must translate the economic reasoning into a story about harm and innovation, keeping the audience oriented toward the policy questions that matter.

A disciplined cross should also test external validity: would the model’s conclusions hold under alternative regulatory environments, or in markets with different competitive dynamics? Ask for tests across geographic regions, time periods, or product categories to evaluate consistency. If results vanish under modest adjustments, insist on an explanation. The expert’s willingness to tour multiple scenarios can reveal overfitting and encouragement of spurious precision. The court benefits from a model that demonstrates resilience across plausible variations, rather than a single tidy, but fragile, outcome.

Another essential line of inquiry centers on the identification strategy. Clarify how causal influence was established and whether confounding variables were adequately controlled. Demand justification for using particular instruments, if any, and assess whether their relevance and exogeneity are defensible. If the identification approach depends on rare assumptions, press for alternative specifications with more transparent reasoning. The cross-examination should steadily erase the aura of inevitability around results and replace it with a transparent, contestable narrative about cause and effect in market behavior.

Finally, prepare to consolidate the critique with a concise, memorable summary. Reiterate the most consequential weaknesses: data limitations, sensitivity to assumptions, and gaps in replication. Emphasize how these flaws could distort settlement dynamics or misguide enforcement priorities. The goal is not necessarily to “win” every point but to compel the opposing expert to acknowledge uncertainty and provide credible bounds. A disciplined close anchors the jury’s understanding and clarifies why the antitrust theory should withstand scrutiny or be revised.

In closing, balance assertiveness with professional restraint to maintain courtroom credibility. Offer a constructive path: suggest alternative models, independent reviews, or pretrial data audits. Your aim is to strengthen the integrity of economic evaluation rather than theatrics. By maintaining methodological discipline, you help ensure that antitrust decisions rest on robust analyses, clear assumptions, and reproducible evidence. The outcome should reflect a rigorous standard of proof, focused on how models perform under real-market conditions and whether the economic theories survive empirical testing.