Skills Data Science Research Design and Causal Identification

Research Design and Causal Identification

v20260724
revacc-methods
A comprehensive guide for establishing credible causal inference in accounting and finance research. This methodology covers advanced techniques including Difference-in-Differences (DiD), Instrumental Variables (IV), Regression Discontinuity (RDD), and experimental design. It instructs users on how to move beyond simple controls, defining the specific identifying variation necessary for publishing rigorous, causality-focused academic work.
Get Skill
323 downloads
Overview

Research Design & Identification (revacc-methods)

When to trigger

  • Your treatment (a disclosure, a standard adoption, an audit/tax regime) may be endogenous
  • Adoption is staggered across firms/years and you need a defensible DiD
  • You have an association and a referee will ask "is this causal or just correlation?"
  • You are designing an experiment to isolate a channel archival data cannot separate
  • You are building an analytical model and must fix primitives, timing, and the equilibrium concept

Identification for archival accounting at RAST

Accounting treatments — disclosure choices, conservatism, auditor selection, tax positions, standard adoption — are rarely random, so RAST referees expect a credible identification strategy, not kitchen-sink controls. Pick the design that breaks the endogeneity for your accounting setting.

Identification threat / setting Design
Regulation / standard adoption with a clean date (e.g., a reporting mandate) Difference-in-differences; event study around the adoption date
Staggered adoption across firms/states/countries Staggered DiD with modern estimators (avoid naive TWFE bias)
Endogenous accounting/auditor/tax choice Instrumental variables / 2SLS with a defensible exclusion
A threshold rule (covenant, index inclusion, size or regulatory cutoff) Regression discontinuity
Selection on observables Matching (PSM/entropy) as a complement, not the main claim
A plausibly exogenous shock to the information environment Natural experiment; pre-trends shown
Information content of an accounting signal Short-window event study with a clean benchmark and confound check

State the estimating equation, the unit and level, the fixed effects (firm, year, industry-year), and the identifying variation explicitly. The design section must make a skeptic believe the variation is as-good-as-random conditional on controls. RAST's first-round-decision culture means a weak design is more likely to draw a reject than a "fix it in revision."

Measurement design is part of identification

For contested accounting constructs (discretionary accruals, earnings quality, disclosure indices, audit quality, information asymmetry), the proxy choice is a design decision. Pre-commit a primary measure with precedent and plan the alternative proxies you will use to show the result is not proxy-driven (carried out in revacc-data-analysis). A clean design on a fragile proxy still fails.

If the lane is analytical

  • Fix the information structure, players, timing, and payoffs before solving; state the equilibrium concept (PBE, sequential, etc.).
  • Show the model is the minimal structure that generates the accounting result; defend each assumption as load-bearing.
  • Plan the comparative statics that become testable or normative accounting implications.

If the lane is experimental

  • Manipulate the focal accounting construct with realistic stimuli and a fit-for-purpose pool (investors, auditors, managers); IRB documentation is expected.
  • Pre-register where feasible; include manipulation and attention checks; power the design for the interaction, not just the main effect.

Design hygiene

  • Show parallel pre-trends for any DiD and report dynamic (event-time) effects.
  • Defend the exclusion restriction for any IV in words — relevance alone is not enough.
  • Pre-commit the main specification; relegate alternatives to robustness.
  • Plan the data provenance trail now (Compustat/CRSP/I/B/E/S/audit-data vintages and screens).

Execution bridge (StatsPAI / Stata MCP)

For the empirical / causal lane, estimate and audit rather than only specify. Full map: execution-with-mcp. RAST is empirical accounting; emphasize identification of disclosure / governance effects and the multiple-testing haircut for mined associations.

  • detect_design → recommend → fit with as_handle=true → audit_result to enumerate the checks the design owes.
  • Panel / staggered DiD: callaway_santanna / sun_abraham + bacon_decomposition
    • honest_did_from_result. IV: effective_f_test + anderson_rubin_ci. RDD: rdrobust + mccrary_test.
  • Experiments: randomization-based inference and romano_wolf for the many-outcome family-wise correction reviewers expect.

Match the toolchain to the reviewer pool, and report the effect size the venue wants. A run end-to-end (synthetic data, real returns) is in the JF execution walkthrough.

Checklist

  • The identifying variation (shock/setting/threshold) is named and defended
  • Estimating equation, unit, level, fixed effects, and clustering plan are stated
  • DiD shows pre-trends and dynamic effects; staggered designs use a modern estimator
  • IV exclusion restriction is argued, not asserted; matching is a complement, not the claim
  • Primary construct proxy pre-committed; alternative proxies planned
  • Analytical models fix primitives/timing and the solution concept before solving
  • Experiments have IRB, realistic stimuli, manipulation/attention checks, adequate power

Anti-patterns

  • Kitchen-sink controls standing in for identification ("we control for everything").
  • TWFE on staggered adoption without addressing heterogeneous-treatment-effect bias.
  • IV by convenience: an instrument correlated with the outcome directly.
  • Matching as causal proof when selection is on unobservables.
  • Proxy fragility: a single contested construct measure with no alternative planned.
  • Non-minimal model: primitives a referee can strip without losing the result.

Output format

【Lane】archival / analytical / experimental
【Setting & identifying variation】...
【Design】DiD / staggered-DiD / IV / RDD / event study / experiment / model
【Spec】equation; unit/level; fixed effects; clustering plan
【Construct】primary proxy + alternatives planned
【Identification defense】pre-trends / exclusion / discontinuity / randomization ...
【Data provenance】sources + vintages + screens noted
【Next skill】revacc-data-analysis
Info
Category Data Science
Name revacc-methods
Version v20260724
Size 6.68KB
Updated At 2026-07-29
Language