text: 264,69,1517308234004058117,17993000,Zach Shahn,"Sorry @KhoaVuUmn. In our new ""working paper"" (are non-economists allowed to have working papers #EconTwitter?), we show that Structural Nested Mean Models are identified under time-varying conditional parallel trends assumptions. (This is good!) 🧵 <LINK> <LINK> Why does this matter? 1) SNMMs are models of time-varying treatment effect heterogeneity. So now we can learn how effects vary as a function of time-varying covariates under DiD type assumptions. 2) We only require parallel trends to hold conditional on time-varying covariates. So if there's a measured time-varying confounder of trends, you can adjust for it under our approach. 3) We can identify additional causal contrasts, eg the effect of one blip of treatment followed by no further treatment, beyond the ITT effect of an initial blip like @CdeChaisemartin and D'Haultfoeuille or the effect of sustained treatment like Callaway and @pedrohcgs 4) Our estimating equations are doubly robust and allow ML estimation of nuisance functions with cross-fitting So this seems to be that mythical free lunch where you can estimate more stuff than the other time-varying DiD methods under weaker assumptions? (Except that in practice to reap some of these rewards you will prob need to correctly specify a parametric blip model.) But we're interlopers in econometrics and the paper is still a work in progress, so I'm really hoping for both feedback and pushback from people like @pedrohcgs, @jmwooldridge, @CdeChaisemartin, @Susan_Athey, and others in what I understand to be the working paper tradition Will add simulations and real data analysis among other things soon, just had to put this out before one of you econ jackals took our idea! Also lots of directions for future work opened up by SNMMs. We mention some in the paper. Very joint work with Jamie Robins, Oliver Dukes, David Richardson, and Eric Tchetgen Tchetgen. I don't think any of them are (publicly) on twitter though. @matt_blackwell @a_strezh @KhoaVuUmn Looking forward to reading it!",https://arxiv.org/abs/2204.10291,"In this paper, we generalize methods in the Difference in Differences (DiD) literature by showing that both additive and multiplicative standard and coarse Structural Nested Mean Models (Robins, 1994, 1997, 1998, 2000, 2004; Lok and Degruttola, 2012; Vansteelandt and Joffe, 2014) are identified under parallel trends assumptions. Our methodology enables adjustment for time-varying covariates, identification of effect heterogeneity as a function of time-varying covariates, and estimation of treatment effects under a general class of treatment patterns (e.g. we do not restrict to the `staggered adoption' setting). We stress that these extensions come essentially for free, as our parallel trends assumption is not stronger than other parallel trends assumptions in the DiD literature. However, in contrast to much of the DiD literature, we only consider panel data, not repeated cross sectional data. ",Structural Nested Mean Models Under Parallel Trends Assumptions,10,"['Sorry @KhoaVuUmn. In our new ""working paper"" (are non-economists allowed to have working papers #EconTwitter?), we show that Structural Nested Mean Models are identified under time-varying conditional parallel trends assumptions. (This is good!) 🧵  <LINK> <LINK>', 'Why does this matter? 1) SNMMs are models of time-varying treatment effect heterogeneity. So now we can learn how effects vary as a function of time-varying covariates under DiD type assumptions.', ""2) We only require parallel trends to hold conditional on time-varying covariates. So if there's a measured time-varying confounder of trends, you can adjust for it under our approach."", ""3) We can identify additional causal contrasts, eg the effect of one blip of treatment followed by no further treatment, beyond the ITT effect of an initial blip like @CdeChaisemartin and D'Haultfoeuille or the effect of sustained treatment like Callaway and @pedrohcgs"", '4) Our estimating equations are doubly robust and allow ML estimation of nuisance functions with cross-fitting', 'So this seems to be that mythical free lunch where you can estimate more stuff than the other time-varying DiD methods under weaker assumptions? (Except that in practice to reap some of these rewards you will prob need to correctly specify a parametric blip model.)', ""But we're interlopers in econometrics and the paper is still a work in progress, so I'm really hoping for both feedback and pushback from people like @pedrohcgs, @jmwooldridge, @CdeChaisemartin, @Susan_Athey, and others in what I understand to be the working paper tradition"", 'Will add simulations and real data analysis among other things soon, just had to put this out before one of you econ jackals took our idea! Also lots of directions for future work opened up by SNMMs. We mention some in the paper.', ""Very joint work with Jamie Robins, Oliver Dukes, David Richardson, and Eric Tchetgen Tchetgen. I don't think any of them are (publicly) on twitter though."", '@matt_blackwell @a_strezh @KhoaVuUmn Looking forward to reading it!']",22,04,2017